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Computer Assisted
Reservoir anagement
bdus Satter
Jim Baldwin
Rich Jespersen
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Disclaimer
The recommendations advice descriptions and the methods in this
book are presented solely for educational purposes.The author and
publisher assume no liability whatsoever for any loss or damage
that results from the use of any of the material in this book.
Use
of
the material in this book is solely at the risk
of
the user.
Copyright 2000 by
PennWell Corporation
1421
South Sheridan Road
Tulsa Oklahoma 74112-6600
US
800.7523764
1.918.831.942
www.pennwellbooks.com
www.pennwell.com
Marketing Manager: Julie Simmons
National Account Executive: Barbara McGee
Director: Mary McGee
Production Operations Manager: Traci Huntsman
Cover and Book Designer: Morgan Paulus
Library of Congress Cataloging-in-PublicationData Available on Request
Satter Abdus Jim Baldwin and Rich Jespersen
Computer-Assisted Reservoir Management
ISBN 978-0-878147-77-9 o tcover
CIP DATA
if
available
All rights reserved. No part of this book may be reproduced stored
in a retrieval system or transcribed in any form or by any means
electronic or mechanical including photocopying and recording
without the prior written permission of the publisher.
Printed in the United States of America
3 4 6 7 8
12 11 10 09 08
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This book grew out of
a
course developed and presented at Texaco over
the past few years. It was eventually presented
s
an SPE short course with
the same title. Our goal is to provide the fundamental background for the
concepts and to share examples of the computerized techniques that we
believe are the key to optimizing the management of oil and
g s
reservoirs in
today's competitive business environment.
The concepts discussed here are not new. Geoscientists and engineers
have long dreamed of the ability to create mathematical models
o
their
reservoirs with which they could try out various operating scenarios before
actually implementing the most profitable plan. The tremendous improve-
ments in computer performance have made it possible to enhance software
to fulfill those dreams.
Previously in the domain of specialists using expensive mainframe com-
puters, many valuable software tools are now readily available on the desk-
top of the practicing geoscientist and petroleum engineer. This is both a
blessing and a curse. It allows the analysis of reservoir data to be done very
rapidly and much more thoroughly than previously. However, at the same
time, it places a greater responsibility on the practitioner to understand the
principles behind a wide variety of reservoir management tools. It is all too
easy to put some numbers into the black box and come up with results that
are quite meaningless. Hopefully, the basics presented in this book will serve
to inform the reader about the vast array of software tools available to assist
him or her. We also hope to be able to stimulate a greater communication
among colleagues of all disciplines to ensure that the data are thoroughly
understood and software is used s intended. Teamwork is more important
than ever if we are to manage our reservoirs in a way that will make our proj-
ects profitable and our companies successful.
Abdus Satter James
0
Baldwin Ricbard A Jepm en
X V
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ble
ListofFigut es
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
i
List of Tables
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
mi
Acknowledgments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
m
Chapter One Introduction.................................
1
Scope Objective
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
Organization
3
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Chapter Two Reservoir Management.........................
Reservoir Management Definition .........................
6
Reservoir Life Process
................................... 7
Integration and Teamwork ...............................
8
Organization and Teamwork 9
Reservoir Management Process 12
Setting the goal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
Developing a plan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Implementation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
Surveillance and monitoring
......................... 18
Evaluation ...................................... 18
References
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
Chapter Three ata Acquisition Analysis
and
Management . . . . .21
DataType . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Data Acquisition and Analysis
...........................
23
Data Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Data Storing and Retrieval 24
Preface
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ I I
...
Forewatd. . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .. . . . . . . . . . . .
Overview
1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
History of Reservoir Management
......................... 6
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Data pplication 25
Chapter our Reservoir Model 29
Role of Geoscience and Engineering 3
Geoscience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Engineering 32
Integration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
References
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
Chapter ive Well Log nalysis 37
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
LogTypes 39
Caliper log 39
Lithology
log
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
Resistivity
logs
...................................
4
Porosity logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
Example nalysis with Integrated Sofiware 3
Data entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Log
data preparation...............................
Well log analysis 44
Cross sections
46
Summations
..................................... 46
References
48
Chapter ix Seismic Data nalysis
.........................
53
Introduction
53
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
nalysis Procedure 43
Seismic Measurements and Processing......................
Structural Interpretation................................
58
Stratigraphic Interpretation..............................
61
3-D
Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
References
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
Chapter Seven Mapping and Data
Visualization
5
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
ControlData ........................................ 66
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DataEntry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
Mapping Sobare Example ............................. 66
Modeldata 69
Alternative displays 69
Display items control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Chapter Eight eostatistical Analysis
........................
73
Conventional Mapping
75
Map Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
76
Measuring Uncertainty................................. 85
Introduction
73
Kriging
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
80
Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
81
Extension to Three Dimensions and to Co-Kriging 7
Conclusions 89
References 92
Chapter
Nine
ell Test Analysis
...........................
93
Introduction ........................................ 93
TestTyp s
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
Transient Fluid Flow Theory ............................ 95
Typical drawdown curve ............................ 97
Buildup test analysis
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
98
Type-Curve Analysis
...................................
99
Essential Questions Asked Before Starting a Well Test Analysis 106
Conventional Analysis
.................................
96
Example problem
................................
102
Reservoir questions 106
Well questions .................................. 108
Fluid questions .................................. 108
Checks During Analysis
108
Model diagnostic
................................
108
WorkBench Well Test Analysis Sobare . . . . . . . . . . . . . . . . . . . 09
Well test analysis................................. 110
Data validation.................................. 108
Matching and final results
..........................
109
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Well test design 112
References 113
Chapter Ten Production Performance Analyses . . . . . . . . . . . . . . . 15
Oil and asReservoirs................................ 115
Natural Producing Mechanisms .........................
116
Reservoir Performance Analysis
..........................
117
Reserves
...........................................
118
References
.........................................
120
Introduction
.......................................
121
Reservoir Volume .................................... 122
Chapter Eleven Volumetric Method ........................ 121
Original Oil and as in Place
...........................
126
Freegas or gascap................................. 126
Oil in place 126
Solution gas in oil reservoir ......................... 127
References
.........................................
128
Chapter Twelve Decline Curve Method ..................... 129
DedineCurves...................................... 130
Decline Curve Equations .............................. 132
Analysis Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Standard method ................................ 136
Ershaghi and Omoregie method
.....................
138
References ......................................... 140
Chapter Thirteen Material Balance Method. . . . . . . . . . . . . . . . . . 41
Oil Reservoirs 142
as Reservoirs
......................................
143
Material Balance Examplecase Study
144
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Chapter Fourteen Reservoir Simulation
.....................
151
Reservoir Simulation 151
Concept and well management ...................... 151
Technology and spatial and time discretization. . . . . . . . . . .152
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Phase and direction of
flow
152
Fluid
flow
equations ..............................
153
Reservoir Simulators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Simulation Process 155
Abuse
of
Reservoir Simulation 156
Golden Rules for Simulation Engineers....................
157
References 160
Chapter Fifteen Computer Software 161
Stand-Alone Sofnvare. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Integrated Software Packages............................ 162
PetroTechnical Open Sofnvare Corporation. . . . . . . . . . . . . . . . . 64
Computing Facilities 165
Internet 165
Field Development
Plan
181
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
181
Development and Depletion Strategy
.....................
184
Reservoir Data 185
Reservoir Modeling 186
Production Rates and Reserves Forecasts . . . . . . . . . . . . . . . . . . . 187
Facilities Planning
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
191
Economic Optimization 192
Implementation 192
Monitoring. Surveillance. Evaluation
....................
193
Reference
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
193
North ApoiIFuniwa .................................. 195
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
195
Objectives
199
Challenges 199
Approach
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
201
Deliverables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Examples
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
160
Chapter Sixteen Case Study l-Newly Discovered
Chapter Seventeen Case Study 2-Mature Field Study
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Decline Curve Analysis
................................
203
Classical Material Balance.............................. 205
Reservoir Simulation
.................................
205
Contributions
......................................
209
Conclusions........................................ 211
References ......................................... 211
Chapter Eighteen Case Study Waterflood
Project
Development
................................
213
Introduction ....................................... 213
ReservoirData 215
Reservoir Modeling .................................. 217
Production Rates and Reserves 221
Economic Evaluation
.................................
225
References ......................................... 232
Chapter Nineteen Mini-Simulation Examples
. . . . . . . . . . . . . . . .
33
Introduction ....................................... 233
Single Vertical Well 237
Single-Lateral Horizontal Well .......................... 240
Single Well-Water Coning
............................
245
5-Spot Waterflooding
.................................
249
References ......................................... 253
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hapterO N E
IN T R O D U T I
O V R V I W
Sound reservoir management prac-
tice involves goal setting, planning,
implementing, monitoring, evaluating,
and revising unworkable plans. Success of
a project requires the integration
of
peo-
ple, technology, tools, data, and multi-dis-
ciplinary professionals working together
as a well-coordinated team.
Integrated computer software plays a
key role in providing reservoir performance
analysis, which is needed to develop
a
management plan, as well
as
to monitor,
evaluate, and operate the reservoir. It is also
useful in day-to-day operational activities.
A major breakthrough in reservoir
modeling has occurred with the advent of
integrated geoscience (reservoir descrip-
tion) and engineering (reservoir produc-
tion performance) sohare designed to
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E S E R V O R d N N G E M E N T
P U T E R A S S S T E D
manage reservoirs more effectively and efficiently. Several service, software,
and consulting companies have developed and are marketing integrated soft-
ware installed on a comm on platform. Users from different disciplines can
work with the s o h a r e cooperatively as a basketball team, ra ther than pass-
ing the ir datdresu lts like batons in a relay race.
This book presents various techniques used in reservoir performance
analysis. Since the results depend on the quality of the reservoir model,
emphasis will be placed on how to build a more reliable reservoir model-
involving geophysics, geology, petrophysics, and engineering. The role
played by integrated computer software in developing a reservoir manage-
ment plan, as well
as
in monitoring, evaluating, and opera ting the reservoir,
will be discussed. Th e presentation will inc lude integra tion of geoscience and
engineering data and the use of integrated software for analyzing full-field
performance, as well as single vertical or horizontal well production, coning
well, and w aterflood pattern performance.
SCOPE ND O B J E C T I V E
This book is the third one in the series of the reservoir management
books, which Satter and Thakur started to publish several years ago. Th e
first book, Integrated Petroleum Reservoir Management, was followed by
Integrated WaterjZoodAsset Management? G rt ai n basic information from the
first book was used in the second book, and has also been used in the third
book. In this way, the follow-up books can be treated
as
stand alone books.
A
team of
two
engineers and a geoscientist wrote the current book in
order to address both geoscience and engineering aspects of computer sofi-
ware used in reservoir studies. T he authors possess more than 100 years of
diversified domestic and international experience in reservoir studies, reser-
voir operations, and management.
This book is not written for just highly experienced engineers, but for
geoscientists, field operation staffs, managers, government officials, and oth-
ers who are involved with reservoir studies and management. It provides an
overall knowledge of the various s o h a r e in use fo r reservoir analyses.
College students in petroleum engineering, geoscience, economics, and
management can also benefit from this book.
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Numerous geoscience and engineering computer software are used to
provide reservoir performance analysis that is needed to develop a manage-
ment plan,
as
well
as
to monitor, evaluate, and operate the reservoir. This
book brings out the role, use, and importance of the computer software in
managing the reservoirs. It is intended to help the user develop his own
common sense approach how best to utilize computer software to manage
reservoirs. There is no “one size fits ll” approach as each reservoir is unique.
It will be the first book to illustrate how geoscience, engineering data,
and computer software can be integrated to analyze reservoir performance
under various scenarios for developing economically viable projects. Our
intention is not to invent new wheels, but to package under one cover the
knowledge and hands-on experience acquired through many years of our
professional careers to demonstrate how the wheels run.
The book is not written to give the readers hands-on experience in run-
ning the software. It explains what the users need to know about the “black
box” behind the software by providing the basic knowledge and under-
standing of the problems being solved. We provide examples of how differ-
ent
types
of computer software are run, including the input data and output
results. For example, it does not really teach how to carry out log interpreta-
tion, or how to carry out a pressure transient analysis. However, it does
inform the reader about what types of Software are available to do their par-
ticular job, and what
c n
be expected.
In essence, it will present the following:
Reservoir management concepts/methodology
Techniques needed for reservoir performance analysis
Examples to illustrate reservoir management using computer sohare
O R G N I Z T I O N
The opening chapter presents an overview of the book, scope and objec-
Our stepwise thought process in organizing the book is given below:
At the outset, chapter
2
on integrated reservoir management provides
tive, and organization.
3
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the background necessary to grasp the use of computer software in
managing the reservoirs.
build the integrated reservoir model (chapter
4 ,
which provides the
foundation for reservoir performance analysis.
geostatistics, and well test analyses provide the basic knowledge
and applications of the computer software.
Chapters 10,
1
1, 12, 13, and
14
deal with production performance
analysis techniques, i
e.,
volumetric, decline curve, material balance,
and reservoir simulation.
Chapter
15
provides an overview of the available stand alone and
integrated computer sohare.
Examples of
full
field studies around the reservoir cycle are given in
chapter
16
for a newly discovered field development plan, chapter
17
for a mature field study, and chapter
18
for a waterflood project
development.
Mini-simulation can play an important role in everyday operations.
Chapter
19
deals with examples of a single vertical or horizontal
well, coning well, and waterflood pattern performance.
Chapter 3 deals with geoscience and engineering data needed to
Chapters 5,
6, 7, 8,
and
9
on well
log,
seismic, mapping,
R E F E R E N E S
1.
Satter,
A
and Thakur,
G.
C.:
Integrated Petrohm
Reservoir
Management:
2.
Thakur,
G.
C., and Satter,
A.: Integrated WaterJh oodAsset Management,
A Team Approach,
PennWell
Books,
Tulsa, Oklahoma
1994)
PennWell Books Tulsa, Oklahoma 1998)
4
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Chapter Two
RESERVOIR
M N GEMENT
C O N C E PT S N D P R C T I C E
Integrated reservoir management has
received significant attention in the indus-
try in recent years through various techni-
c l
articles and sessions, seminars, and
even publication of a book. The need to
increase recovery from the vast amount of
remaining oil- and gas-in-place worldwide
requires better reservoir management prac-
tices to maintain a competitive edge. The
principal goal of reservoir management is
to maximize profits from a reservoir by
optimizing recovery while minimizing
capital investments and operating expens-
es. Economically successful operation of a
reservoir throughout its entire life from
discovery to abandonment requires:
Integration: merging geoscience
and engineering people, technology,
tools, and data
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Synergy: multidisciplinary professionals working
as
a
well-
coordinated “basketball team” rather than
as a
“relay team”
Support: company culture and organization removing barriers and
fostering teamwork and integration
Satter and Thakur presented in their book’ sound reservoir management
concepts and methodology, technology needed for better reservoir manage-
ment and examples to illustrate effective reservoir management practices.
Satter, Wood, and OrtizZ resented a brief review of the conventional
as
well
as
the state-of-the-art concepts and approaches for integrated asset opti-
mization and, more importantly, focus on the practice of improved asset
management with real life examples.
This chapter discusses briefly the fundamentals of integrated reservoir
management. It is provided because the readers need to know about the
reservoir management goal, process and methodology so
that they can
have
a
better understanding of the role of computer software in managing
the reservoirs.
RESERVOIR M N G E M E N T DEFINITION
Reservoir management can be defined as the process used to add value
to the asset. It is no longer enough to just manage the reservoir itself. The
focus is now on adding value to company assets by managing them in the
context of the upstream, midstream, and downstream businesses. Integrated
efforts and close working relationships between professionals knowledgeable
in the areas of reservoir management, engineering, basic science, research
and development, service, environment, land, legal, finance, and economics
are essential.
H I S T O R Y O F
RE SE RV OIR M N GE M E NT
Reservoir management has advanced through various stages in the
past 30 years. Before 1970, reservoir engineering was considered to be the
most important technical aspect of reservoir management. During the
1970s
and
1980s,
the benefits of synergism between engineering and
6
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geology were recognized; the benefits of detailed reservoir description, uti-
lizing geological, geophysical, and reservoir simulation concepts was realized.
RESERVOIR LIFE P R O C E S S
A reservoir’s life begins with exploration, followed by discovery, delin-
eation, development, production, and finally abandonment Fig.
2-
1 .
Multidisciplinary professionals, technologies, and tools are involved in the
reservoir work.
Discovery
Delineation
Exploration
Abandonment
Development
Tertiary
Secondary Primary
Production
Fig.
2 1
Reservior Life Proc ess
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I N T E G R T I O N N D
T E M W O R K
Successful operation throughout the life of the reservoir requires inte-
gration
of
geoscience and engineering, ie. people, technology, tools, and
data Fig. 2-2 . It also requires synergy, ie. multi-disciplinary professionals
working together as a team, rather than as individuals Fig. 2-3 .
People
Data
Management Geological
Geoscientists Geophysical
Engineers Engineering
LandLegal Financial
Field
Financial\
Integration
I
Tools
Technology
Seismic Interpretation Seismic
Tomography Geologic
Data Acquisition Geostatistics
LogginglCoring Engineering
CompletionsLFacilities
Geologic Modeling
Pressure Transient Environmental
Fracturing Computer
Reservoir Simulators
Enhanced Oil Recovery
Computer Software Hardware
Drilling Completions
Enhanced Oil Recovery
Fig.
2 2
hfegration of Geoscience and Engineering
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Fig. 2 3 Multi Disciplinary Reservoir Management Team
O R G N I Z T I O N N D
T E M W O R K
The organization and management of assets is also very critical. In the
old system, various cross-disciplinary members
of
a team worked on an asset
under their own functional management Fig. 2 4 . In the new multidisci-
plinary team approach, asset management team members from various func-
tions work with a team leader whose responsibility is to coordinate all activ-
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IVISION
MANAGER
Fig.
2 4
Old and New Organizations
ities and report to the asset manager (Fig. 2-4 . Administrative and project
guidance is provided by the production manager or the asset manager. The
asset management concept emphasizes focussing on a field(s) as an asset and
all team members have the primary objective of maximizing the short- and
long-term profitability
of
the asset.
Technology departments had to better align themselves closely with
operating divisions of the company. Functional and organizational changes
have been made within technology organizations in order to serve the busi-
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R E S E V O I R M A N A G E
E N T
I
F O N C E P T S N D T R C T I C E
Fig. 2 5 Texaco
E
P Technology Organization
ness units better and add value to the com pany assets. Figure
2-5
shows an
example of Texaco’s E
P
Technology Department organization, which is
based on customer focused portfolios. T he role of the portfolios in Texacds
organization is to design, develop, and deliver technology products and tech-
nical support.
R ES ER VO IR M N G E M E N T P R O C E S S
The modern reservoir management process consists of setting the goal,
developing, implementing, and monitoring the plan, then evaluating the
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results Fig.
2-6 .
No
component
of
the process
is
independent
of
the oth-
ers, therefore, integration of these components is essential for successful
results. It is dynamic, ongoing, and no different than any other management
process, such as financial, health, or business.
Revising
Fig. 2 6 Reservoir Management Process
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R E S E
V O I R M A N A G E
E N T
f O N C E P T S A N D Y R A C T I C E
Sett ing the go l
Th e reservoir management goal can vary depending upon the company
Maximizing the econom ic value of an asset Fig. 2-7)
Maximizing employment in the national industry
Conserving natu ral energy effectively
strategy being supported. Some examples include:
(
Maximize Profits
Profits
Capital
Investments
(
Make Choices
-
Let
It
Happen
Production
-
Make It Happen
Rate Primary
Secondary
Tertiary
Economic
Economic
Economic
Time
Fig.
2 7
What is Reservoir Management?
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In all of these cases, the conventional wisdom has been to optimally use
all available resources, ie. human, technological, data, and financial, to
maximize profits from
a
reservoir by optimizing recovery while minimizing
capital investments and operating expenses.
In more recent years, the trend has been to set the goal and make it hap-
pen by whatever means possible,
eg.
new technologies, improved team work,
joint ventures, and inter- and intra-company partnering and alliances.
Developing
plan
A
comprehensive planning process will greatly improve the chances
of
successful reservoir management. It needs to be carefully worked out, involv-
ing many time-consuming development steps Fig.2-8).
Flg.
2 8
Developing Plan
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RESE
V O I R
M A N A G E E N T
f O N C E P T S
A N D
T R A C T I C E
The nature of the reservoir being managed is vitally important in setting
its management strategy. Understanding the nature of the reservoir requires
knowledge of the geology, rock and fluid properties, fluid flow and recovery
mechanisms, drilling and well completion, and past production performance
Fig. 2-9). Reservoir knowledge is gained through an integrated data acquisi-
tion and analysis program to be discussed in chapter
3.
Geology
Rock
S W
Fluid
Flow
Recovery Mechanisms
Fluid
P
Past Performance
QO
Fig.
2 9.
Reservoir K nowledge
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Traditionally, data of different types have been processed separately,
leading to several different models-a geological model, a geophysical
model, and a production/engineering model. The reservoir model is not
just an engineering or geoscience model, rather it is an integrated model,
prepared jointly by geoscientists and engineers,
as
will be discussed in
chapter
4.
The economic viability of a petroleum recovery project is greatly influ-
enced by the reservoir production performance under current and future
operating conditions. Evaluation of the past and present performance and
forecast of its future behavior are essential aspects of the reservoir manage-
ment process (Fig. 2-10).
Fig. 2 10 Production and Reserves Forecast
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High-technology black
oil,
compositional, and enhanced oil recovery
numerical simulators are now playing a very important role in reservoir man-
agement.
As
opposed to one reservoir life, the simulators can simulate many
lives for the reservoir under different scenarios and thus provide a very pow-
erful tool to optimize reservoir operation. Results of the forecasts are useful to
monitor, evaluate, and operate the reservoir Fig. 2-11).
I
Fig.
2 11 Perform ance Evaluation
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P U T E R
s s
I S T E D
E S E R V O I R
M A N A G E M E N T
Facilities that are the physical link to the reservoir include drilling, com-
pletion, pumping, injecting, processing, and storing. Proper design and
maintenance of the facilities has a profound effect on profitability. The facil-
ities must be capable of carrying out the reservoir management plan, but
they cannot be wishlly designed.
Economic optimization is the ultimate goal selected for reservoir man-
agement. Chapter
18
presents an example illustrating the key steps involved
in economic optimization.
Implementation
Once the goal and objective have been set and an integrated reservoir
management plan has been developed, the next step is to implement the
plan. Success in implementation can be improved by starting with a flexible
plan of action, management support, and commitment of field personnel.
Surveillance and monitoring
Sound reservoir management requires constant monitoring and surveil-
lance of the reservoir production, injection, and pressure data, in order to
determine whether the actual performance is conforming to the plan Fig. 2-
11).
A
successful program requires coordinated efforts of the various func-
tional groups working on the project.
Evaluation
How well is the reservoir management plan working? The answer lies in
the careful evaluation of the project performance. The actual performance,
e.g., reservoir pressure, gas-oil ratio, water-oil ratio, and production, needs to
be compared routinely with the expected Fig.2-1
1).
Any discrepancy should
be resolved and adjustments made by utilizing integrated team efforts.
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R E F E R E N E S
1
Satter, A. and Thakur, G . C.:
Integrated P etrohm Reservoir
Management: A Team Approach PennWell Books,
Tulsa, Oklahoma (1994
and Practice ”JPT
August, 1998
2.
Satter, A., Wood, L.,
and
Ortiz, R.: “Asset Optimization Concepts
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Chapter T
H
R
E E
D A T A ACQUISIT ION
I
ANALYS IS
N D ~ ~ G € ~ €
The knowledge of the reservoir being
managed
is
vitally important in setting
reservoir management strategies. This chap-
ter presents geoscience, engineering, and
productiodinjection data needed to build
the integrated reservoir model that will be
discussed in chapter4, providing the foun-
dation for reservoir performance analysis. In
addition, data acquisition, analysis, valida-
tion, storing, retried, and application
wll
be presented in
thii
chapter.
Throughout the life of
a
reservoir,
from exploration to abandonment Fig. 2-
l , enormous amounts of data are collect-
ed. An efficient data management pro-
gram, consisting of acquisition, analysis,
validating, storing, and retrieving,
c n
play a key role in reservoir management
Fig.
3-1).
It requires planning, justifica-
tion, prioritizing, and timing.
An
integrat-
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Fjg.
3 1 Data Acqu is i tion and Analysis
ed approach involving all functions
is
necessary to lay down the foundation
of reservoir management.
Satter and Thakur provided a thorough discussion of this topic, which
is summarized here.
D T
T Y P E
The various types of data that are collected before
and
after production
(Fig.
3-1)
can be broadly classified
s
listed in Table
3-1.
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Seismic:
Geological:
Rock:
Fluid:
Well Test:
Production:
Injection:
Enhanced
Oil Recovery:
A N A L Y S I S I
A N D A A M A G E M E N T
D A T A A Q U I S I T I O N
2-D 3D and cross-well tomography
Despositional environment diagenesis lithology
structure faults and fractures
Logging Open hole and cased hole
Coring Conventional whole core and side wall
Pressure-Volume-Temperature
Transient well pressure
Oil gas and water
Gas and water
Data applicable to thermal miscible and
chemical floods.
Table
3 1
Reservoir Data Classification
D T C Q U I S I T O N A N D N L Y S 1S
Multi-disciplinary groups, i.e. geophysicists, geologists, petrophysicists,
drilling, reservoir, production and facilities engineers, are involved in col-
lecting various types of data throughout the life of the reservoirs. Land and
legal professionals also contribute
to
the data collection process. Most of the
data, except for the production and injection data, are collected during
delineation and development of the fields.
n effective data acquisition and analysis program requires careful plan-
ning and well-coordinated team efforts of interdisciplinary geoscientists and
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engineers throughout the life of the reservoir. Justification, priority, timeli-
ness, quality, and cost-effectiveness should be the guiding factors in data
acquisition and analysis. It is essential to establish specification of what and
how much data are to be gathered, and the procedure and frequency to be
followed. It will be more effective to justify data collection to management,
if the need for the data, costs, and benefits are clearly defined.
D T V L ID T ION
Field data are subject to many errors, e.g. sampling, systematic, random,
etc. Therefore, the collected data need to be carefully reviewed and checked
for accuracy, as well s for consistency.
In order to assess validity, core and log analyses data should be carefully
correlated and their frequency distributions made for identifying different
geologic facies. The reservoir fluid properties can be validated by using equa-
tion of state EOS) calculations and by empirical correlations. The reason-
ableness of geological maps should be established by using knowledge of the
depositional environment. The presence of faults and flow discontinuities, s
evidenced in a geological study, can be investigated and validated by pressure
interference and pulse and tracer tests.
The reservoir performance should be closely monitored while collecting
routine production and injection data, including reservoir pressures. If past
production and pressure data are available, classical material balance tech-
niques and reservoir modeling can be very useful to validate the volumetric
original hydrocarbons-in-place and aquifer size and strength.
Laboratory rock properties, such
s
oil-water and gas-oil relative per-
meabilities, and fluid properties, such
s
pressure-volume-temperature
PVT) data, are not always available. Empirical correlations can be used to
generate these data.
D T
S T O R I N G A ND
R E T R I E V L
The reconciled and validated data from the various sources need to be
stored in a common computer database accessible to
all
interdisciplinary end
users.
As
new geoscience and engineering data become available, the data-
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base requires updating. The stored data are used to carry out multipurpose
reservoir management functions, including monitoring and evaluating the
reservoir performance.
Storing and retrieval of data during the reservoir life cycle poses a major
challenge in the petroleum industry today. The problems are:
incompatibility of the sobare and data sets from the different
lack of communication between databases
disciplines
Many oil companies are staging an integrated approach to solving these
problems. In late 1990, several major domestic and foreign oil companies
formed Petrotechnical Open Sobare Corporation
POSC)
to establish
industry standards and a common set of rules for applications and data sys-
tems within the industry.
POSC s
technical objective is to provide a com-
mon set of specifications for computing systems, which will allow data to
flow smoothly between products from different organizations and will allow
users to move smoothly from one application to another.
D T PPLIC TION
Seismic data, especially
3-D,
has gained tremendous importance in reser-
voir management in recent years
s
it provides valuable details of reservoir
structure and faulting. Improvements in both recording and processing tech-
niques, coupled with advancements in interpretation and visualization soft-
ware, c n reveal heterogeneities in complex reservoirs and even of the fluids
within the rocks in some cases. The
3-D
seismic information coupled with
cross-well tomography also provides information on interwell heterogeneity.
Geological maps, such s gross and net pay thicknesses, porosity, perme-
ability, saturation, structure, and cross-section, are prepared from seismic,
core, and log analysis data. These maps, which lso include faults, oil-water,
gas-water, and gas-oil contacts, are used for reservoir delineation, reservoir
characterization,well locations, and estimates of original oil- and gas-in-place.
The well log data, which provide the basic information needed for reser-
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voir characterization, are used for mapping, perforations, estimates of origi-
nal oil- and gas-in-place, and evaluation of reservoir perforation. Production
logs can be used to identif) remaining oil saturation in undeveloped zones
in existing production and injection wells. Time-lapse logs in observation
wells can detect saturation changes and fluid contact movement.
Also,
log-
inject-logs can be useful for measuring residual oil saturation.
Unlike log analysis, core analysis gives direct measurement of the for-
mation properties, and the core data are used for calibrating well log data.
These data can have a major impact on the estimates of hydrocarbon-in-
place, production rates, and ultimate recovery.
Core analysis is classified into conventional, whole-core, and sidewall
analyses. The most commonly used conventional or plug analysis involves
the use of a plug or a relatively small sample of the core to represent an inter-
val of the formation to be tested. Whole core analysis involves the use of
most of the core containing fractures, vugs, or erratic porosity development.
Sidewall core analysis employs cores recovered by sidewall coring techniques.
The fluid properties are determined in the laboratories using equilibri-
um flash or differential liberation tests. The fluid samples can be either sub-
surface sample or a recombination of surface samples from separators and
stock tanks. Fluid properties can be also estimated by using correlations.
Fluid data are used for volumetric estimates of reservoir oil and gas,
reservoir type, i.e. oil, gas or gas condensate, and reservoir performance
analysis. Fluid properties are lso needed for estimating reservoir perform-
ance, wellbore hydraulics, and flow line pressure losses.
The well test data are very useful for reservoir characterization and reser-
voir performance evaluation. Pressure build-up or falloff tests provide the best
estimate of the effective permeability-thickness of the reservoir, in addition to
reservoir pressure, stratification,
and
the presence of faults and fractures.
Pressure interference and pulse tests provide reservoir continuity and barrier
information. Multi-well tracer tests, used in waterflood and in enhanced oil
recovery projects, give the preferred flow paths between the injectors and pro-
ducers. Single well tracer tests are used to determine residual oil saturation in
waterflood reservoirs. Repeat formation tests can measure pressures in strati-
fied reservoirs, indicating varying degrees of depletion in the various zones.
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DA
T A
AQ U I s T I
O M
I A N A L Y S I S
A N D M A N A G E M E N T
Commonly used reservoir performance analysis and reserve estimation
techniques that will be presented in chapters
10
through 14are:
volumetrics
decline curves
material balance
mathemetical simulation
In addition to geological, geophysical, petrophysical, and reservoir engi-
neering data, production and injection data are needed for reservoir per-
formance evaluation.
R E F E R E N C E S
1.Satter, A. and T hakur, G. C.: Integrated Petroleum eservoir
Management: A Team Approach
PennWell Books
Tulsa, Oklahoma 1
994)
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Chapter
Fou
R
R E S E R V O I R M O EL
The economic viability of a petrole-
um recovery project is greatly influenced
by the reservoir production performance
under the current and future operating
conditions. The accuracy of the results
derived from the techniques used for ana-
lyzing reservoir performance and estimat-
ing reserves is dictated by the quality of
the reservoir model used to make reservoir
performance analysis. An integrated reser-
voir model, which is the subject of this
chapter, requires a thorough knowledge of
the geology, geophysics, rock and fluid
properties, fluid flow and recovery mech-
anisms, drilling and well completions, and
past production performance.’ The basis
for this knowledge is the data acquisition,
analysis, and management, s discussed in
the preceding chapter.
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RO L E
O
G E O S I E N E
N D
ENGINEERING
Traditionally, data of different types have been processed separately,
leading to several different models-a geological model, a geophysical
model, and a production/engineering model. The reservoir model is not
just an engineering or a geoscience model-rather it is an integrated model,
prepared jointly by geoscientists and engineers. The importance of geo-
science and engineering data is discussed below. The many data sources
required to adequately describe a reservoir can be seen in Table 4-1. The
challenge is to integrate all this information into a model that reasonably
describes reservoir performance.
GEOSCIENCE
Geoscientists probably play the most important role in developing a
reservoir model. The distributions of the reservoir rock types and fluids
determine the model geometry and model type for reservoir characterization.
The development and use of the reservoir model should be guided by
both engineering and geological judgments. Geoscientists and engineers
need feedback from each other throughout their work. For example, core
analyses provide data to verify reservoir rock types, whereas well test analysis
can confirm flow barriers and fractures recognized by the geoscientists.
By
discussing all the data
s
a team, each specialist can contribute the data
he/she has available and can help other team members understand the sig-
nificance of that data.
Three-dimensional seismic data can be used to assist in:
defining the geometric framework
qualitative and quantitative definition of rock and fluid properties
flow surveillance
A
3-D
seismic survey impacts the original development plan. With the
drilling of development wells, the added information is used to refine the
original interpretation.
As
time passes and the data builds, elements of the
3 0
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D T
STRUCTURE ISOPACH MAPS
POROSITY, PERMEABILITY,
FLUID
SATURATIONS
FLUID
CONTACTS
FORMATION TOPS
RESERVOIR PRESSURE
TEMPERATURE
PVT PROPERTIES
RELATIVE PERMEABILITIES
PRODUCTION RATES
HISTORY
R E S E R V O I R
IYO EJ
c
SOUR E
3D SEISMIC WELL LOGS
WELL LOGS, CORES,
CORRELATIONS
WELL LOGS
WELL TESTS
BOTTOM HOLE SAMPLES
CORRELATIONS
CORES CORRELATIONS
WELL TEST ALLOCATION
SUMMARY
Table 4 1 Data Sources
3-D data that were initially ambiguous begin to make sense. The usefulness
of
a
3-D seismic survey lasts for the life of a reservoir.
Three-dimensional seismic surveys help identifj. reserves that may not
be produced optimally. T he analysis can save costs by m inimizing ry holes
and poo r producers.
A
3-D survey shot during the evaluation phase is used to assist in the
design of the development plan. With the development and production,
data are constantly being evaluated to form the basis for locating production
and injection wells, managing pressure maintenance, performing workovers,
etc. These activities generate new information (logs, cores, DSTs, etc.),
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changing and/or revising previously generated maps, structures, stratigraph-
ic models, etc.
Geostatistical modeling of reservoir heterogeneities is playing an impor-
tant role in generating more accurate reservoir models. It provides a set of
spatial data analysis tools as a probabilistic language to be shared by geolo-
gists, geophysicists, and reservoir engineers, as well
s
a vehicle for integrat-
ing various sources of uncertain information. Geostatistics is usel l in mod-
eling the spatial variability of reservoir properties and the correlation
between related properties such as porosity and seismic velocity. A geosta-
tistical model can then be used to interpolate a property whose average is
critically important and to stochastically simulate for a property whose
extremes are critically important.
Geostatistics enable geologists to put their valuable information in a for-
mat that can be used by reservoir engineers. In the absence of sufficient data
in a particular reservoir, statistical characteristics from other more mature
fields in similar geologic environments and/or outcrop studies are utilized.
By capturing the critical heterogeneities in a quantitative form, one is able to
create a more realistic geologic description.
ENGINEERING
After identifjring the geologic model, additional engineeringjproduction
data are necessary for completion of the reservoir model. The engineering
data include reservoir fluid and rock properties, well location and comple-
tion, well-test pressures, and pulse-test responses to determine well continu-
ity and effective permeability. Material balance calculations can provide the
original oil in place, and natural producing mechanisms-including gas cap
size and aquifer size and strength. The use of injection/production profiles
provides vertical fluid distribution.
INTEGR TION
Integration of geoscience and engineering data is required to produce
the reservoir model, which can be used to simulate realistic reservoir per-
formance (Fig. 4-1 .
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R E S E R V O I R M O D E
GEOPHYSICS
GEOLOGY
GEOST TISTICS
ENGINEERING
PETROPHYSICS
Fig.
4 1
Integrated Reservoir Model
Development of a sound reservoir model plays a very important part in
the reservoir management process because:
It requires integration among geoscientists and engineers.
It allows geoscientists’ interpretations and assumptions to be
compared to actual reservoir performance
as
documented by
production history and pressure tests.
3 3
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It provides a means
of
understanding the current performance
and to predict the future performance of a reservoir under various
what if conditions so that better reservoir management decisions
can be made.
In addition, the reservoir model should be developed jointly by geosci-
entists and engineers (Fig. 4-2 because:
An
interplay of effort
results
in better description of the reservoir
and minimizes the uncertainties of a model. The geoscientists' data
assist in engineering interpretations, whereas the engineering data
sheds new light on geoscientists' assumptions.
Assemble complete data set
Share understanding
of
data
Realize measurement uncertainties
Refine model as new data are gained
Fig.
4 2
Data Sharing and Validation
3 4
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R E S E R V O I R M O D E a
The geoscientist-engineer team can correct contradictions
s
they
arise, preventing costly errors later in the field's life.
In a fragmented effort,
i.e.,
when engineers and geoscientists are not
in communication with one another, each discipline may study only
a fraction of the available data, thus, the quality of the reservoir
management can suffer, adversely effecting drilling decisions and
depletion plans throughout the life of the reservoir.
opportunities to tap unidentified reserves. For example, improved
3-D seismic data can aid in surveillance of production operations in
mature projects and c n identify presence
or
lack of continuity.
between wells,
and
thus improves the description of the reservoir model
One very important factor, which is often overlooked, is to discuss
measurement uncertainties. Should a number be taken s hard fact,
or is there a range of possible values around the given value?
Utilizing reservoir models developed by multi-disciplinary teams
can provide practical techniques of accurate field description to
achieve optimal production.
Multi-disciplinary teams using the latest technology provide
A
major breakthrough in reservoir modeling has occurred with the
advent of integrated geoscience (reservoir description) and engineering
(reservoir production performance) software designed to manage reservoirs
more effectively
and
efficiently. Several service, software, and consulting
companies are now developing and marketing integrated software installed
in a common platform (chapter 15 .These interactive and user-friendly
software packages provide more realistic reservoir models. The users from
different disciplines can work with the software cooperatively s a baseball
team rather than passing their own results like batons in a relay race.
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REFERENCES
1. Satter
A.
and Thakur,
G. C.:
Integrated Petroleum
Reservoir Management: A eam Approach, PennWell
Books
Tulsa, Oklahoma
1
994
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Chapter
F
I V E
I
W L L LOG 7
N
L
YS
IS
I N T R O D U T I O N
Measurements taken in the wellbore
by wireline logging devices provide criti-
cal data for understanding and describ-
ing the reservoir. The properties dis-
played at measured depths on the log
curves represent combined responses of
the rocks and fluids in the formation, as
well as tool geometry and the type of
fluid in the borehole.
Typical measurements recorded on
well logs include:
spontaneous potential
natural gamma radiation
induced radiation
resistivity
acoustic velocity
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density
caliper
But the ultimate analysis results needed for reservoir management are:
producing zone depths
zone thicknesses
rock types
porosities
permeabilities
fluid saturations
Most of the required information cannot be measured directly. It must
be inferred from the log responses by making certain assumptions about the
interaction of the logging device with the rocks and fluids.
Before this analysis can be done, the logs must be adjusted or “environ-
mentally corrected for the effects of the
tool
geometry and borehole fluids.
Since any particular log may detect one reservoir condition and be insensi-
tive to another, several types of logs are usually compared to determine the
true nature of the reservoir.
Besides the digital curve values, additional information from the well is
required for proper formation evaluation. Some of the additional data is
found on the log header of the paper log. It includes:
mud resistivity
mud temperature
mud density
mud composition
borehole temperature
tool geometry
The comment portion of the log is used by the logger to report impor-
tant information on the tool operation, such
as:
malfunctions
hole conditions
logging company name
3 8
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W€h
L O G
N L Y S I S
It is necessary to know which logging company recorded the data, since
the environmental corrections are different for each company’s tools. The
original log tape may contain all this information, but digitized
log
tapes
usually do not. So the paper log headers and comments may be needed.
L O G
T Y P E S
There is a large and growing number of logging tools available to aid in
the determination of reservoir properties. Understanding the details of their
operation and the subtleties of interpreting the measurements is the domain
of the log analyst. Below we list a few of the basic logs and group them into
categories according to the results they provide.
aliper log
Measurement of borehole diameter by the caliper log reveals deviations
from actual bit size. Increases in diameter indicate washouts of unconsolidat-
ed beds. Surprisingly, the borehole can also become smaller than the bit size.
This results from the buildup
of
a mud cake in zones that are permeable
enough to take the water from the drilling mud. The caliper is also used to
indicate where pad type tools may give inaccurate readings due to either rapid
change in hole size, or hole size too large for pad contact with the formation.
Other logs are broadly categorized into three types: lithology logs, resis-
tivity
logs,
and porosity logs.
Lithology logs
Spontaneous po ten tia l SP). This tool measures electrical currents that
occur naturally when fluids of different salinities are in contact. Permeable
mnes are invaded by filtrates from the drilling mud. Since the salinity of the
reservoir fluid generally is different than that of the drilling mud, SP currents
are generated by the interaction of the two fluids. This tool is normally used
with fresh water-based drilling muds.
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The generated potential (in millivolts) is given by:
SP= 60 .133
T
og ( Rmf/R w )
where:
T = temperature, “F
Rmf mud filtrate resistivity, ohmmeter
R
=
water resistivity, ohmmeter
If
all
the other parameters are known, this equation can be solved for
R .
But relative values, rather than absolute
SP
readings, are used when inter-
preting the log curve. Impermeable shale zones can be used to set a “100
shale base line” from which one can measure the relative magnitude in SP
variations. The
SP
deflection is a qualitative indicator of permeability, but it
is reduced by shale or hydrocarbon content.
Gamma ray GR).
Natural radiation of the formation (usually due to
clay or shale content) is recorded by the gamma ray tool.
It
can be used when
SP is not applicable and is therefore useful in detecting and evaluating
deposits of radioactive minerals. In sedimentary formations, the gamma ray
log, which has an average depth of penetration of one foot, reflects the over-
all shale content of the formation. This is due to the fact that the radioactive
deposits usually concentrate in clays and shales. Clean formations generally
have a very low level of radioactivity. This log is frequently used as
a substi-
tute for the
SP
in cased holes where the
SP
in unavailable or in open holes
where the
SP
is unsatisfactory.
The gamma ray log can be recorded in cased holes. That makes it quite
useful in completion and workover operations. It is often used for correla-
tions and, when combined with a casing collar log, allows for accurate posi-
tioning of perforating guns. The gamma ray log is also often used in con-
junction
with
radioactive tracer operations.
Resistivity logs
Much information about the reservoir can be deduced from measure-
ments of electrical resistivity. The classic equation of Archie gives the water
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saturation,
S,,,
as
a function of porosity,
4
water resistivity,
Rw,
and true for-
Here, a, m nd n are experimentally determined constants. The param-
eters vary, but are often approximately
a =
1, m
=
2, and n = 2. This rela-
tionship works well in clean formations, but not as well in shaly formations
or formations in which the connate water is fresh. Resistivity is used to deter-
mine water saturation (since only water in the rocks is usually conductive).
The conductivity is proportional to the NaCl content of the water, to tem-
perature and to water volume (porosity). Sometimes resistivity is used as the
base log to pick formation tops and thicknesses and to correlate with other
wells. The formation resistivity factor is defined as F
=
R / R where R is
the resistivity of a formation
100
saturated with water of resistivity R .
Electric
hg.
This classic log consists of an
SP
curve and a combination
of resistivity curves designated normal or lateral.
Induction-electrichg Formation conductivity is measured by this log,
which is a combination of SP or GR, 18”normal, and induction curves.
It
works best for medium to high porosity.
Dzlal indudon
hg.SP and/or GR and three resistivity curves (three depths
of investigation) combine to make the dual induction log. Resistivity curve sep-
aration indicates invasion of drilling fluid into the formation. The difference is
reduced, however, by the presence of hydrocarbons. The short device measures
the flushed zone, Rm, hile the medium induction curve measures both flushed
and invaded Ri)
ones, and the deep induction measures primarily the uncon-
taminated true reservoir resistivity
Rt).
The ratios of shallow/deep and medi-
um/deep yield d (diameter of invaded zone),
Rm,
nd
Rr.
Guard
h g hterohg).Guard electrodes focus the formation current into
a thin disk in this log, which is used in conductive muds, thin beds, and high
resistivity formations.
Porosity
logs
h o w t i c velocity h g sonic h g ) .The difference in travel time, At, from
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a sound source at one end of the tool to two receivers at different distances
along the tool is measured. This difference represents the travel time of
sound through the portion of the reservoir between the two receivers. Both
the rock matrix and the fluid in the pores influence the result, resulting in
an estimate of the porosity,
qk
This
log
is good for primary porosity, but does not indicate
all
secondary
porosity. It yields high porosity in shaly zones.
Density
hg.
A
similar-looking estimate for porosity is obtained from the
measurement of the electron density of the formation using a chemical
source of gamma radiation and two gamma detectors:
8 =
Pmr
-A n
Pmr
PflLdd
where
p
represents density. This
log
works in air-drilled holes or with
any type of fluid. Its penetration is shallow,
so
ppuds usually taken to be
drilling fluid density
(1
.O for fresh mud, 1.1 for salt-base mud). The densi-
ty
log
results in pessimistic Sw n the presence of shale (unless corrected), but
the porosity is not much affected by shale. On the other hand, the density
log yields high porosity in the presence of gas.
Neutron
hg. Induced radiation is measured by bombarding the forma-
tion with fast moving neutrons. As these neutrons collide with hydrogen
nuclei in the formation fluids, they lose energy and eventually are captured,
causing the emission of a gamma ray. The number of gamma rays is propor-
tional to the amount of hydrogen in the formation. Since the concentration
of hydrogen in oil and water is similar, the result is
an
estimate of fluid-filled
porosity. Shales indicate high neutron porosity due to bound water (can be
used as a shale indicator). asmnes indicate low neutron porosity.
Combinationporosity hgs. Comparing the measurements from two or
more porosity tools can resolve the uncertainties presented by the individual
devices in differentiating liquids from gas, identifying lithology, and deter-
mining shale volume. Both acoustic velocity and density have a weak
response to gas while neutrons indicate a much lower porosity for gas-filled
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N L Y S I S
rock than for fluid-filled rock. Density, acoustic velocity, and neutrons
all
respond differently to lithology. The ratios of density-derived porosity to
acoustic velocity or neutron porosity yield the shale volume.
ANALYSIS P R O C E D U R E
The steps involved in performing the log analysis include data entry, data
preparation, the analysis itself, and verification of the results,
as
detailed below:
1. Data entry
loading log curves
entering other logging information
quality control, trace edit, and depth shift
borehole environmental corrections
2. Log data preparation
3.
Well
log
analysis
parameter selection
computation of reservoir properties
4 Verification of results with core, drill cutting, and other wells
The procedure is essentially the same (except for data loading) whether
done with paper logs or by computer. The following example illustrates the
procedure when an integrated software package is used.
EXAMP LE ANALYSIS
W I T
INTEGR TEDOF TWARE
Some integrated applications include not only the facilities needed to
analyze well-log data for single wells, but also additional capabilities for
analyzing entire reservoirs. For example, with Baker-Hughes/SSI’s
Petroleum WorkBench, the reservoir description module can perform
all
the following functions:
Data entry
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Log
data preparation
- trace editing and depth shifting
- environmental corrections
Cross
sections
Integration of core analysis
Well log analysis
-
parameter selections
-
saturation equation selection
Summations
Mapping and contouring
Volumetrics
Note the similarity with the analysis procedure outlined above.
A
few
key steps in the computer analysis are illustrated below:
Data
entry
The log traces are imported as files or digitized from paper. Then sup-
plementary information about the logging run, including mud properties
and
tool
typ s
are keyed into forms
as
shown in Figure 5-1.
Log
data
preparation
interactively to any bad data segments. Curves may also be shifted to align
them properly with each other.
The raw logs are examined
as
in Fig.
5-2)
and corrections are made
Well log
analysis
Data contained in the logs can be graphically displayed in various ways
(Fig. 5-3 o aid the determination of parameters and cutoffs for the analy-
sis. Then the analysis is run, converting the recorded data into the desired
logs of
rock and fluid properties, as shown in Figure
5 4 .
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Fig. 5-1
Entry
of
Logglng Information
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P U T E R A S S I S T E D
ES E R V U R M N G E M E N T
Fig. 5-2 Raw Logs Ready for Analysis
Cross
sections
Cross sections of the raw logs can be used to correlate geologic tops from
well to well and isolate the zone to be analyzed. After the analysis, cross sec-
tions of results logs can be displayed as in Figure 5-5.
Summations
Geologic layers are defined on the cross sections. Then the summation
process determines the representative value of each log property in each layer
for each well. These results are tabulated for one well in Figure
5-6.
The
results for all wells are also automatically displayed on the associated prop-
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Fig.
5-3
Analysis Plots for Lithology Parameters
erty maps of each property in each layer ready for contouring.
An
example
of one such property map is show n in Figure
5-7
which can be used to gen-
erate contour maps.
The beauty of this computer analysis is that all the steps are performed
in one program minimizing the often time-consuming task of loading and
exporting data from one program to another.
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Fig.
5 4
Results of Log
nalysis
R E F E R E N E S
Some good sources of additional information on log analysis are
Introduction
to
Wireline
Log
Anabsis,Western Atlas
Log
Interpretation Principles and Applications Schlumberger
Open Hole AnaLysis and Fornation Evaluation Texaco
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Fig.
5-5 Log Cross-Section
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Fig. 5-6 Sum m ation Results Table
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N L Y S
IS
t
well
0.269
well 3
0.271
well 2
0.266
Fig. 5-7
Posted Porosity Values from Logs
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Chapter
S x
S E
SM
c
D
T A
N L Y S I S
I N T R O D U C T I O N
Seismic data, especially 3-D has
gained tremendous importance in reser-
voir management in recent years.
Traditionally, 3-D seismic provided valu-
able details of reservoir structure and
faulting. Improvements in both recording
and processing techniques, coupled with
advancements in interpretation and visu-
alization software, have revealed seismic
expression of heterogeneities in complex
reservoirs and even of the fluids within the
rocks in some cases.
Information we can usually determine
from modern 3-D seismic data includes:
depth to reservoir
structural shape, faulting, and
salt boundaries
visualization of reservoir
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1 2 f 2
.
1
2
Fig.
6 1
The Seism ic Record Section
In some cases it may also
be
possible
to
gain additional insight into:
porous interval identification
hydrocarbon reservoir identification
geologic history
overpressured zone identification
properties between wells
movement of fluids
fracture orientation
The
following discussion briefly explains basic seism ic concepts to pro-
vide a better understanding of the interpretation.
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[ S E I S M I D A T A
A N A L Y S I S
S E I S M I C
M E S U R E M E N T S
A N D P R O C E S S I N G
Seismic reflection occurs at an interface where rock properties change,
as
illustrated in the depth diagram of Figure 6-1.This simplified example of
seismic recording shows the ray path of energy from shot 1 being partially
reflected
at
the top of each rock layer and recorded at receiver
1.
The display
of the resulting “seismic trace” number
1 is show n on the cross-section of the
time diagram in the figure, where each reflection appears as a “wavelet” at
the point corresponding to its arrival time at the receiver. T he procedure is
repeated, with shot and receiver
2
moved a few meters down the line, and
so
on. For efficiency in actua l field recording, several hundred receivers spread
along the line or over an area are recorded simultaneously from each shot.
Later, traces with a “common reflection point” are gathered together and
averaged
to enhance the signal in a single display trace).
T he size of the recorded wavelet depends on the reflection s trength, o
which is not determined just by the properties within one rock layer, but
rather by the contrast in acoustic impedance which equals velocity densi-
ty) of the
two
layers above and below the interface Fig.
6-2):
Normal Incidence Reflection
i +
p = b i
P i
V1 rock layer
interface
t
=
l+Ro)i
P2
v
R
= P2
v2
- P1 VI
P2 v2 P1
Vl
0
Fig.
6 2
Seism ic Reflect ion A m plitude
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P U T E R A S S I S T E D
E S E R V O I R MA N A G EMEN T
where:
p = bulk density of the upper layer
p
= bulk density of the lower layer
vL
= acoustic velocity in the upper layer
v2 =
acoustic velocity in the lower layer
o = the reflection strength at
0
angle of incidence, i.e.,
perpendicular to the interface
In Figure
6-2,
“i“
represents the incident amplitude at the interface,
“r”
is the reflected amplitude, and
“t”
is the transmitted amplitude. Their relative
values are indicated qualitatively by the sizes of the corresponding arrows.
Figure
6-3 is an example record section in which color represents ampli-
tude. Changes in color along any particular “event” indicate variations in
rock or fluid properties, either above or below the interface. If we can assume
the layer above the interface is a uniform cap rock, then the amplitude
changes can be attributed to the reservoir itself.
Fig.
6 3
Seismic Amplitude
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I S E I S M I D A T A
A N A L
Y S
I s
It
is important t o be aware of the vertical resolution in seismic data. T h e
“shot” whose energy is recorded creates a wavelet of finite length. As the
wavelet travels through the rocks, high frequency com ponents are attenuat-
ed more than low frequencies, causing the wavelet to get longer. By the time
it reaches the receivers,
it looks som ething like Figure 6-4. Sowavelets arriv-
ing from the tops of adjacent thin layers overlap and interfere with each
other on the recorded trace, making i t impossible to resolve thin layers.
T h e geometry of seismic ray paths m ust also be considered. If the rock
layers are dipping, the true location of the reflection is no t known. The trace
is plotted
as
if all reflections occurred directly below the source-receiver mid-
point, as shown in Figure
6-5. A
further processing step, called “migration”
is required to move the dipping events to their proper locations.
T h e recorded reflection event is also influenced by its travel through
the shallower rocks, which m ay transmit the signal with little distortion or
may alte r i t severely. So the degree to which reservoir properties are visible
= 15 ft
cyc
v
1 0 0 0 0 f t l s e c
f
6 7 c y c l s e c
a= -=
Fig 6 4 Vertlcal Resolution
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in seismic data varies with the geologic setting. When conditions are right,
a significant contribution to the reservoir description can be gained from
seismic data.
S T R U C T U R L INTERPRET
Figure
6-6
shows a typical seismic cross section from an offshore area.
The horizontalax is is map position. The verticalaxis is 2-way travel time for
the seismic waves sort of a non-linear depth scale). The color or shade of
gray indicates the strength of the reflected signal.A lot of information about
the geology is apparent to the trained eye.
Figure
6-7
shows the same data with some faults interpreted and the
outline of a salt
dome indicated. By correlation with well logs if available),
the interpreter will determine which of the seismic events corresponds to the
Field Recording
Seismic Record Section
Fig 6 5 Migration
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S E r s M r c
D A T A
A N A L
Y S
I s
reservoir and follow
it
throughout the
3-D
volume, producing a structure
map of the top-of-reservoir. This map is usually plotted in seismic travel
time, but may be converted to depth using velocities extracted from special
velocity surveys and/or from the raw seismic data.
Conventional seismic data processing makes the simplifying assumption
that the rocks are composed of horizontal layers, and that the reflection
point in the subsurface is half way between the seismic source and the receiv-
er. When this is approximately the case, seismic traces can be processed and
displayed as a function of seismic travel time. Traces having a common
reflection location, but a different source-to-receiver distance, are “stacked”
summed to reduce noise) to image the rock layers clearly, without having to
first create a detailed model of the rock velocities
as
they vary both horizon-
tally and vertically.
Fig.
6 6
Seismic Cross Section
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When there are gradual structural changes in the rocks, these assump-
tions still work reasonably well.
An
additional process called
post-stack time
migration
is then required to make adjustments in structural positions and
shapes,
as
discussed in Figure 6-5.
In areas
of
complex geologic structure, the assumptions used in conven-
tional processing do not hold. This causes the images to be poorly focused
during the stacking process. They look “fuzzy” and/or dipping reflection
images are not positioned correctly. To solve this problem, geophysicists have
developed a method for converting the data from seismic travel time to
depth
and
doing the migration before the stack. This
pre-stack depth migra-
tion
requires an iterative procedure of estimating a model of the rock veloc-
ities and using it to image the data. Then using the image to give a better
Fig.
6 7
Seismic Interpretation
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A N A L Y S I S
A T A
estimate of the velocity model, which is used to get a better image, and
so
on. When the model is correct, the pre-stack depth migration image is
sharply focused and reflections are properly positioned.
Figure
6-8 shows a comparison of time m igration and dep th migration.
Note the vertical scale difference and the non-linear stre tch of the time sec-
tion due to velocity variations. Such improvem ents in seismic images make
it easy to understand why pre-stack depth migration has become the norm
in complex areas.
Post-Stack
Time
Migration
W -St ack Depth Migration
Fig
6 8
Time Migration vs. Depth Migration
S T R A T I G R A P H I C
N T E R P R E T A T I O N
Once a seismic horizon has been interpreted, its reflection strengths
also called seismic amplitudes) c n be displayed as
a
map. Th e example in
Figure
6-9
is
from a shallow horizon. It shows evidence of a buried stream
channel, which was filled with sand or m ud having different acoustic imped-
ance than the surrounding rocks. Whenever a heterogeneous rock layer is
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Fig.
6 9
Seismic Amp li tude Map
overlain by a uniform layer, there is the possibility that the changes in densi-
ty
or acoustic velocity will be visible in such a seismic reflection strength map.
Variations of porosity and of the fluids filling that porosity also alter the
acoustic impedance of the reservoir rock. So anomalies in rock porosity or
fluid content can sometimes be detected indirectly in seismic amplitude
maps. Such maps c n be used directly to position future wells. They can also
be used
to guide the characterization in a reservoir model.
3 D V I S U A L I Z A T I O N
Three-dimensional visualization is a tool for displaying 3-dimensional
data on a 2-dimensional screen. The data can be rotated, panned, and
momed. Motion often makes the shapes easier to understand. Seismic sur-
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veys, geostatistical3-D grids, and sim ulation models can be loaded into the
s o h a r e to enhance seismic interpretation and reservoir management.
Com mercial 3-D visualization s o h a r e allows the user to interactively
examine an entire volume of data in one image. Starting with an opaque
view of a complete volume, the user can adjust the transparency to visualize
the spatial distribution
of
selected attributes e.g., amplitudes) and extract
structural relationships from the data set. Alternatively,
an
interpreter c n
choose
a
range of values for an attribute, select one “seed point,” and have
the s o h a r e “auto-pick” all connected data with similar values.
An
example
of auto-picking is shown in Figure
6-10,
where part of the seismic data has
been stripped away after two horizons were picked. Auto-picking and 3-D
Reservoir A
Cut-Away
of
Seismic Volume
with Auto-Picked
Horizons
Reservoir B
Fig. 6 10 3 0
Visualization
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visualization cannot only decrease interpretation time by orders
of
magni-
tude but can also greatly improve the interpreter’s understanding of the reser-
voir revealing detailed information about reservoir quality and heterogeneity.
R E F E R E N C E S
Suggestions for further reading:
Brown Alistair R: Interpretation
of
3-Dimensional Seismic Data,
Jenkins Waite and Bee The Leading Edge, Sept 1997
PG Memoir 4
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I N T R O D U T I O N
For many years, computer sofnvare
has provided tools for combining and dis-
playing data from geologic studies, seismic
surveys, and well
logs
Maps of spatial
data include important auxiliary informa-
tion, such
as
lease boundaries and cultur-
al descriptions. Various plots, charts, and
maps help the interpreter better under-
stand the results of decline curve analysis
and reservoir simulation, such
as
well pro-
ductivity and fluid saturations.
Computerized mapping programs are
commonly available and c n save a great
deal of time, as well as enhance accuracy
of display and understanding of the data.
A
word of caution is in order, however.
While fdly automatic maps using default
parameters can be handy for a quick
look
and data editing, they are generally not
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suitable for model description in simulation or for reserve calculations.
Contouring algorithms are only as good as the data supplied to them.
They cannot incorporate critical conceptual information, which is often
needed to make the results “geologically reasonable.”
Every computer generatedmap should be carefilly checked. Manua l editing
should be done as needed to ensure that the “ t is correct and appropriate f i r
ts
intendedpurpose.
C O N T R O L D T
The manual editing needed to prepare acceptable maps is done by
adding “control data” to guide the computer contouring especially at map
edges). Several types
of
control data can be entered:
Well values-values of
a
property in a layer at a well spot
Control contours-digitized from a paper map, or drawn freehand
Control points-like control contours, but single points
Faults-digitized or drawn
on the screen by the user to change the generated contour
D T E N T R Y
Data c n be input to mapping programs in several ways. These include:
digitizing
importing files
keying in
screen editing, using a mouse
M PPING S O F T W R E E X M P L E
Some of the features of a typical mapping program are illustrated in the
following figures, which were created with Baker Hughes/SSI’s Petroleum
WorkBench.
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H A P P N G A N D
D T I
J I S U A L I Z A T I O N
isplay items control
The display can usually be thought of as an overlay of many layers of
information. Options allow viewing of selected items, properties,
nd
model
layers, such as
Surface components
well surface spots
well names
base map lines
base map text
Fig. 7 1 Base Map
with
Structure Control
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base map scale
lease lines/names
grid volumetrics, simulation)
Layer components
well layer spots
well values
control contours
control points
faults
generated contours
Grid cell values show numbers or paint color)
Fig
7 2
Computed Structure Contours
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I A P P N G A N D D T
J I S U A L I Z A T I O N
Full zoom control is also generally available. Figure
7-1
illustrates some
of the types of data commonly included in a computer-generated map.
M O D E L
D T
Control data consisting of array data, input points, input contours,
faults, and/or well values are used as the basis for contouring. Unless the user
specifies otherwise, faults are used only when contouring “structure-depend-
ent” properties. Figure
7-2
shows structural contours computed independ-
ently in each fault block from the input control data.
L T E R N T I V E D I S P L Y S
Once the map values have been calculated, there are several ways to dis-
play the results. The contours shown in Figure 7-2 are similar to a hand-
made map. But with computer software, there are further capabilities.
Various color-fill displays of such
2-D
maps are common. Alternatively,
a
3-
D display, such
as
Figure 7-3, may help to visualize the surface. Such displays
Fig 7 3
3
Mesh
Plot
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can be tilted and rotated.
A
so-called
4 D
display is shown in Figure
7 4.
Here the mesh shows the structure in 3 dimensions, while the addition of
color allows simultaneous display
of
porosity
Many types
of
computer display are available. Recently, dramatic
increases in workstation computing speed and data capacity have allowed the
development of
“3-D
visualization” software that was the stuff of dreams
only a few years ago. Geoscientists and engineers, working with sofnvare
developers, are inventing new techniques for “seeing” what the data have to
reveal about the nature of hydrocarbon reservoirs and their behavior under
various operating scenarios. With these new capabilities, speed of interpreta-
tion is increasing by orders of magnitude, with concurrent increases in depth
of understanding.
One dramatic example of these developments is in
3-D
seismic data
interpretation. In some reservoirs, increases in the amplitude of seismic
Fig 7 4 40 Mesh Plot
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reflections are directly related to the presence
of
hydrocarbons. The new
sofi-
ware allows instantaneous selective viewing
of
the data. By displaying only
the highest amplitude data and rendering all other data “transparent,” the
interpreter c n immediately see the shape and extent
of
the hydrocarbon
accumulation within such a reservoir. The same technique
c n
be used with
the output of a reservoir simulator to visualize the distribution of fluids
throughout the reservoir at various
stages of
production.
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G E O S T T S T I C A L
A N A L Y S I S
I N T R O D U C T I O N
The mapping described in chapter 7
is a simple extension of the maps one
would make by hand contouring.
An
alternative procedure measures statistical
variations
of
the data points and then cre-
ates maps having similar statistical prop-
erties throughout. This procedure is
called “geostatistics,” because it
was
first
used in the mining industry to map geo-
logical properties.
Like conventional mapping, geostatis-
tics seeks to answer the question “What are
the reservoir property values between the
wells?” Fig. 8-1). But analysis
of
the sta-
tistical distribution of data values
can
lead
to more detailed estimates of map values
between measured points, better describ-
ing the true heterogeneity
of
the reservoir.
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The usual measure of this statistical variation is called a “variogram.”
It
is a mathematical expression involving the measured data points, which rep-
resents how the data values change from one location on the map to anoth-
er. The amount
of
change is measured
as
a function of the distance between
data points, and sometimes as a function of direction. The variogram serves
as a tool for interpolating the map property between known points, while
preserving the degree of variation seen in the data.
Another benefit of geostatistical mapping is that it provides a means for
displaying the uncertainty of the interpolated values-an important piece of
information that is not available from conventional maps.
A third advantage of geostatistical mapping is that
it
can integrate inde-
pendent measurements of a map property. For example, if both well logs and
3-D seismic amplitude are related to reservoir porosity, a map of porosity can
be made that ties the wells together and also incorporates the trends between
wells that are seen in the seismic data. Thus the correlation of the “hard data”
of the porosity log and the
“soft
data” of the
3-D
seismic are integrated to
create the porosity map.
a
3
?
a 3
3
a
Fig. 8-1 Geosfafisfics- Reservoir Prop erties Between th e Wells?
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I
G EO ST A T S T I C A L
K N
A L Y S I s
The following discussion provides an overview of the geostatistical
method, illustrating the concepts mentioned above by showing a real oil
reservoir example:
C O N V E N T I O N L M A P P I N G
Well log analysis generally provides the measured data that serves as the
basis for mapping reservoir properties such as:
gross thickness
net thickness
porosity
In the Rocky Mountain example of Figure
8-2,
two
formations are iden-
tified in a cross-section through a few of the wells.
Analysis
of
all available logs throughout the field resulted in the map of
net reservoir thickness shown in Figure
8-3.
The task at hand is to estimate
the net thickness at all locations between and surrounding the wells.
4 0
Brushy
Basin
Salt
Wash
Base
Salt
Wash
Fig 8-2 Well Logs Providing Reservoir Properties
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The conventional technique for estimating values is to contour the data,
by hand, or by using a computer algorithm. If hand-contoured by a geologist,
the resulting map can incorporate knowledge of the geologic trends and depo-
sitional patterns. But maps made by
10
different people would likely give
10
somewhat different thicknesses at any point that is not fairly near a well.
Mathematical algorithms can provide consistent means of contouring
the data, with results like those of Figure 8 4. Such algorithms generallycan
not incorporate geologic character unless provided with additional “control”
points and/or contours by the geologist.
M P
S T A T I S T I C S
Geostatistics provides a means of interrogating the data to determine
how a reservoir property varies with distance and direction from any given
Fig. 8-3 Salt Wash Net Thickness Measured at Wells
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I G E O S T A T I S T I C A L
A N A L Y S I S
point.
A
map that includes these trends
can
then be created. Net thickness
is used in Figure 8-5 and the following examples to illustrate the concepts,
which apply similarly to any property that can be mapped.
The common measure of variation with distance and, optionally, direc-
tion is shown in Figure 8-6. It is called a variogram, or to be precise, a semi-
variogram, due to the 1/2 in the equation:
Here Z x) is the net thickness at one well and
Z(x+h)
is the net thick-
ness at a well a distance h away. The difference in net thickness is squared
and added to the squared differences in all other pairs of wells that are sepa-
rated by distance h. Since wells are not usually spaced on uniform incre-
ments, distance of separation is divided into “bins,” such
as
the donuts
shown in the figure. Then
“h”
represents any distance within the donut. The
summation is over the “n” pairs of wells found at this distance. Dividing the
sum by n
gives the average variance.
Fig
8-4
Conventional Contouring
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C O M P U T E R A S S S T E D
I R E S E R V O I R h N A G E M E N T
GEOSTATISTICS uses the spatial correlation
of
measured values of a property to estimate the
value
of
the property of other locations
How does the
net thickness
vary with
distance and
direction from
one well to
another?
Fig 8-5 Spatial Correlation of Data
The result calculated for net thickness for the
n
pairs of wells in the
donut represented by distance h is plotted in Figure
8-7.
The process is
repeated at
all
other distances to complete the variogram.
Several important terms describing characteristics
of
the variogram are
defined in Figure 8-8.
When wells are very close together, we expect their net thicknesses to be
similar. So, as h approaches zero, the variance should approach zero. The
variance at h=O is called the nugget For the types of data normally used in
reservoir studies, the nugget should be zero.
If
it is not, then there
are
not
enough measurements at short distances or there are measurement errors.
As well separation increases, the variance of net thickness typically
increases until a distance is reached where there is no longer any statistical
significance. At this distance, called the
range
the variance tends to flatten.
The variance at the range is called the sill
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G E O S T A T I S T I C A L
I A N A L Y S
I s
The measure
of
spatial correlation is called a
VARIOGRAM. It determines the variation
of
net thickness with distance and direction.
y h) 1 / 2 n )
Z X) Z X
h)f
Fig. 8-6 Calculating a Variogram
Variograms can be calculated independent of direction, so they depend
only on distance, as discussed above. Commonly, it is also desired to know
how reservoir properties vary with direction.
To
do this, the pairs of wells at
distance h are divided into groups depending on the direction from one well
to the other in each pair. Separate variograms are calculated for each group.
In the example shown in Figure 8-9,45 directional bins are used to calcu-
late variograms for N-S, NE-SW, E-W, and NW-SE.
In this example, the net thickness changes rapidly in NE-SW and E-W
directions,
as
shown by the short ranges of those variograms. There is greater
continuity in N-S and NW-SE.
If the ranges of these
four
directional variograms are plotted in their cor-
responding directions, an ellipse can be
fit
to them. This variograrn
ellipse,
as
shown in Figure 8-10, then represents the variation of range in
all
directions.
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CO
P U T E R A S S S T ED
E S E R V O I R
h N A G E M E N T
k- Distance
Fig. 8-7 Variogram
It has its minor axis in the direction of most rapid change of net thickness
and its major axis in the direction of greatest continuity. Note that the minor
and major axes do not correspond exactlyto any of the four calculated direc-
tions, even though they happen to be very close in this example.
K R I G N G
Once the variogram ellipse has been calculated, it can be used by a con-
touring algorithm to guide the estimation of the values of net thickness
throughout the map, as
shown in Figure 8-1
1.
This is called Ar ng. It results
in a map that ties the measured values at the wells and also honors the sta-
tistical trends determined by the directional variograms. Note that contour
shapes tend to mimic the variogram ellipse.
If the nuggets of the variograms were not zero, there would be disconti-
nuities in the map. The trends would not approach the measured values at
the wells, and there would be sudden jumps to tie the wells.
In Figure 8-12, a conventional contouring algorithm is compared to geo-
statistical kriging. Both maps tie the wells, but they differ in the areas away
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G E O S T A T
S T l C A L
I
K N
A L
Y
s
I s
Variance, ylh)
NUGGET y 0)
>RANGE
NUGGET y 0)
Fig
8-8
Variogram Terminology
from the wells, where the conventional contouring does not indicate the geo-
logical trends measured by the variograms and incorporated in the kriged map.
An alternative to contouring is to represent the map values by colors Fig.
8-13).This also reveals the size
of
the grid used for the kriging calculations.
Regardless
of
the method used to create the map, uncertainty in the esti-
mates increases with distance from the wells. The four measured points
shown on the vertical cross-sectionof Figure 8-14 represent well values, and
the double-headed arrows represent the range of possible values at locations
between the wells.
The line through the middle is the kriged value. Kriging creates a smooth
map that ties the wells and estimates the most likely values between the wells.
What kriging does not show is the range of possible values between the
wells, shown in the figure by the two bounding lines.
S I M U L A T I O N
An alternative to the single map produced by kriging is to generate
many maps spanning the range of possible estimates between the bounding
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C O M P U T E R A S S
S T E D
[
R S R V O I R
~ N N A G E M E N T
Fig 8-9 Directional Variograms
lines. This is called simzllation. Simulation creates a mathematical model of
the mapped property that has the same spatial statistics
as
the actual data.
There are many possible maps that differ from each other, but have the same
statistics. Some of them meet the additional condition that they honor the
measured data points (i.e., the maps tie the wells). In the usual case where
the simulation is forced to honor known data points, the process is called
conditional simulation.
Each individual simulation map, called a
realization
s one of many pos-
sible estimates. Maps produced by simulation include more fluctuations
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G E O S T T
S T I C A
L
I
K N A L
Y S
I s
Fig.
8-70
Variogram Ellips e
than kriged maps, because they allow for the extremes in the estimated val-
ues.
No
single map represents the “most likely” case. Instead, the set of all
realizations represents the range
of
possibilities. Each
of
the realizations is
equally likely to represent the actual reservoir condition.
In comparing maps produced by these different methods, note that:
kriging estimates represent a moving average process, and the
simulation produces a series
of
more rapidly fluctuating maps,
resulting map is heavily smoothed
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P U T E R
A s s I S
E D
E S E R V O I R
M A N A G E M E N T
Fig.
8-11
Kriged
Map
Confoured)
which all have the same general features, but exhibit the range
of possibilities
These concepts are most easily understood by looking at an example. In
Figure 8-15, the smooth kriged map is compared to some of the nine real-
izations from a simulation. The number of
realizations is arbitrary. The color
scale is the same for all maps, making it clear that the simulations include
more extreme possibilities among the predicted values between the wells.
Any
of
the realizations is probably a better representation of the texture of
the actual geology, while the kriged map represents the “most likely” value at
any particular location.
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G E O S T A T
S I
C A
- K N A L Y S I S
riging Reflects Data Trends
Conventional Contouring Kriged Contouring
Fig.
8-12
Kriging Reflects Data Trends
M E S U R I N G
U N C E R T A I N T Y
Geostatistics provides an opportunity to measure the uncertainty of the
maps it produces. The kriged map of Figure 8-16 predicts a relatively large
net thickness in the area of a shallow well that did not penetrate this reser-
voir. It has been proposed to deepen the well and complete it here. But how
certain are we of the net thickness shown on this map? The following exam-
ple shows how simulation can be used to determine the uncertainty in the
predicted net thickness and, thus, the risk associated with drilling this well.
The plot on the right in Figure 8-17 is the probabiliq densiqfinct ion
(PDF)
from the simulation for the grid cell containing the proposed well. It
shows that of the nine realizations, two were estimated with a net thickness
of 107 to
117
ft, three were estimated 118 to 128
ft,
and four were estimat-
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Fig.
8-13
Kriged Map
Color
Filled)
ed 129 to 1 40
fi
(Using
a
larger number of realizations would have resulted
in smaller bin sizes here, and a smoother histogram).
The plot on the lefi is the cumulative probability distribution, also
called
czrrnukztivedensityfinction (CDF). It is simply a cumulative graph of
the PDF. Th e vertical axis gives the “probability” that the net reservoir thick-
ness will be less than the corresponding value on the horizontal axis. For
example, there is a 35 probability that the net thickness will be less than
120
fi
and a 90 probability tha t it will be less than 132
fi
These plots represent only the grid cell
at
the proposed well location.
An
alternative display, shown in Figure 8-18, gives the probability of finding net
thickness greater than 125 feet anywhere on the map. W hite areas have no
chance of thickness this large. The proposed location, shown by the dark
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G E O S J J S T I C A L
A H A L Y S I s
ocation
Fig.
8-14 Uncertainty n
Kriging
well symbol, is again seen to have a good chance of success, with about a
55
chance of finding more than
125
feet
of
pay.
E X T E N S I O N TO 3 D I M E N S I O N S
AND T O
C O K R I G I N G
Geostatistics can also:
estimate variation in 3 dimensions
integrate additional measurements
as
either hard or
soft
data
The examples discussed above use geostatistics
to
create maps in 2
dimensions. The same techniques can be extended to 3 dimensions. For
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Fig.
8-15
Realization Comparisons
example, well
logs
provide measurements of reservoir properties over an
interval of depth. In mapping porosity, a vertical variogram can be calculat-
ed, to determine the variance of porosity with sample separation in the logs.
The techniques described above can then make use of both the vertical and
horizontal variograms to populate a 3-dimensional grid with porosity esti-
mates by either kriging or conditional simulation. Figure
8-19
is an example
of a
3-dimensional geostatistical model created from temperature logs dur-
ing a steam flood.
Sometimes additional indicators
of
a reservoir property may be available.
Seismic amplitude might correlate to reservoir porosity, for instance. Since
seismic is an “indicator” of porosity, but not an absolute measure, it can be
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Fig.
8-16 Predicted Net Thickness
treated
as “soft
data” in geostatistical calculations. When the “hard data” at
the wells are used to generate a porosity map, the estimates away from the
wells are modified somewhat to reflect the suggested trends from seismic
amplitude. This can be a
very powerful tool for reducing the map uncertain-
ty
and it
is
especially useful to estimate values between widely-spaced wells.
C O N C L U S I O N S
Geostatistics provides:
a means of estimating reservoir properties that incorporates
measured trends
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Fig. 8-17 Probablility
Plots
for Proposed Well Location
Fig. 8-18 Probabliliiy of Net Thickness,
>
125 feet
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0
100
150
zoo
21
22
23
242
Reservoir Temperature After
Stem
Injection
Fig.
8-79
3-Dimensional Temperature Model
a measure of the uncertainty in predictions
a method for integrating independent measurements of a
reservoir property
We have seen that geostatistics offers
2-
and 3-dimensional methods for
determining likely property values away from points of measurement. These
values are estimated by using the geological trends found in the measureddata.
The statistics of the simulation process provide a means of determining
the uncertainties involved in predicting reservoir properties. These uncer-
tainties can be visualized by plotting numerous “realizations”of the map, by
making probability maps of a particular scenario, and by plotting the calcu-
lated probability distribution functions.
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CO
P U T E R A S S
S T E D
f E S E R V O I R h A N A G E M E N T
If there are multiple types of measurements for the same property,
such as well
logs
and seismic attributes, they can be integrated in the m ap-
making process.
R E F E R E N C E S
1.
DeLage, Jack, Scientific Software-Intercom p, Houston, Texas,
personal comm unication
Two good textbooks on this subject are:
Hoh n, Michael E.: Geostatistics and Petroleum GeoLogy,
Isaaks, Edward H. and Srivastava, R. Mohan:
An
Introduction to
Van
Nostrand R einhold (1988)
Applied Geostatistics, Oxford University Press (1989)
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Chapter N I N E
W L L
TEST
AN A L
Y S
I
s
I N T R O D U C T I O N
Well pressure transient testing
includes the techniques for generating and
measuring time variations of pressures in
wells, while flowing or shut-in. The well
test data are used to determine:
Reservoir properties-permeability
Pressure-bottom hole flowing, and
Wellbore condition-wellbore
and porosity
average reservoir
storage, skin, completion, and flow
efficiency
and size, sealing faults, pinch outs,
and edge-water contacts
Boundaries-reservoir geometry
Interference from other wells
The test results are utilized for explo-
ration, reservoir engineering, and produc-
tion engineering activities.
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This chapter will provide fundamentals of well testing, and some insight
into well testing software, with examples.
T E S T T Y P E S
Pressure tests in wells usually consist of:
drawdown and buildup tests on a production well
fall-off tests in an injection well
interference tests involving
two
or more wells, e.g.,
an
injector and
surrounding producing wells
drillstem tests on temporarily completed new wells
As fluids are produced at a constant rate in a drawdown test (Fig.9-1),
reservoir pressure decreases in a manner that depends on reservoir properties
fP time
.i
builduperiod
period
tp
time
Fig.
9 1
Drawdown and Buildup
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W E L L T E S T
I A N A L Y S I S
and conditions in and near the wellbore. Advantages of drawdown tests
include no loss of production during testing and the potential for determi-
nation of oil-in-place with extended tests. The initial pressure,
pi,
s an
important reservoir parameter that is measured directly. Disadvantages
include the requirement that the rate be held constant, which is impractical
and rarely achieved.
For a buildup test, the well is shut in and the pressure begins to return
to normal (Fig. 9-1 . Advantages include precise control of rate (zero) and
the ability to determine p , the “extrapolated pressure.” Disadvantages
include lost (delayed) production due to the required shut in.
In an injection test, pressures are measured while injecting fluids into the
reservoir. In case of afall-oftest, the well is shut in after a period
of
constant
injection, measuring the drop of pressure with time. These tests correspond
to the producing well buildup and drawdown tests.
A drillstem test prior to well completion consists of a series of drawdown
and buildup tests to sample the formation fluid and establish the probabili-
ty of commercial production.
An inte erence test consists of measuring pressures in a shut in well, while
the wells surrounding the test well are producing. This type of test provides
information on reservoir connectivity, and preferential reservoir flow patterns,
which can not be obtained from single well drawdown and buildup tests.
T R A N S I E N T FLUID
F L O W
T H E O R Y
The mathematical basis for pressure analysis methods is the unsteady-
state or transient fluid flow theory in a porous medium, which can be
obtained by using (1) law of conservation of mass, (2) Darcy’s law, and
(3)
equation of state.
The resultant radial flow for a slightly compressible fluid is given by the
Difisivity Equati~n”~*~
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where:
p
= pressure
r = radial direc tion of flow
t =
time
k
=
permeability
4
= porosity
p
=
fluid viscosity
c
=
fluid compressibility
The nam e comes from the similar equation, derived much earlier, which
applies to the d iffusion of heat. If the right hand side of this equation is zero,
we have steady state cond itions and this becomes
Darcy’s Equation.
C O N V E N T I O N A L A N A L Y S I S
Earlier conventional techniques for analyzing well test data, including
Horner, and Miller, Dyes and Hutchinson, are applicable for tests of SUE-
cient duration, if and when radial flow is established in the reservoir and
boundary effects are not too i m p ~ r t a n t . ~hese methods are essentially based
upon solutions of the diffusivity equation.
Th e most common and useful of the many solutions of the d i h i v i t y
equation is called the constant terminal rate solution, for which the initial
and boundary conditions are:
p
=
piat t =
0
for all r
p
=
pi t r
=
q =
constant at r
=
r, for all t (line source)
An
approximation of the constant terminal rate solution for long times
for all t (infin ite reservoir)
and vanishing wellbore is:
000264kt o.351
log,, lCr,2
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W E L L
TEsr
A N A L Y S
I
s
I
where:
p ,
=
wellbore pressure, psia
pi
=
initial reservoir pressure, psia
q =
flow rate, STB/D
p =
viscosity, cp
B =
formation volume factor, RB/STB
k = permeability, md
h
=
zone thickness,
f t
4 =
porosity, fraction
r, =
wellbore radius, f t
c
=
compressibility, psia-
t
=
time, hr
This expression for the time-dependent well pressure due to constant
rate production is the basic equation used in well pressure test analysis.
According to this equation, a plot of wellbore pressure versus logarithmic
(base
10)
time should yield a straight line for a test, say drawdow n, if the
reservoir producing rate is constant.
Typical drawdown curve
A schematic representation of well pressure behavior in the case of draw-
down is shown in Figure 9-2. I t may be observed that the regions of the plot
at early and late times are no t straight but curved. Early time is affected by
conditions at or close to the well. Middle time , which is straight or virtual-
Iy straight, is influenced by the inter-well conditions. Late time is affected by
interference from adjacent wells and/or reservoir boundaries.
The middle time slope can be used to determ ine formation flow capac-
ity (permeability x thickness), kh. For example, slope, m, n the drawdown
curve above is approx imately
500
psi over
2
cycles. The flow capacity can be
easily calculated as follows.
From Figure 9-2:
162.6qM SOOpsi
kh 2cycle
1)2=--- -
- - 25Opsi cycle
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3000
.+ 2000
&
3
1000
0
Early Time
:
near wellbore
:
properties
.
wellbore
&
’ .
I .
MiddleTime
, “infinite-acting’’ : .
, reServ:
I
Late Time
characteristics boundary
&
interference
effects
I
0.1 1 o 10.0
100.0 1000.0
log) TIME,
hrs
Fig. 9 2 Drawdown Curve
if:
= 153
STB/D
p =
0.8
cp, and
B =
1.38
RB/STB
then:
162.6~153~0.8~1.38
250
=
109.9
mD-ft
h =
Buildup test analysis
The solution to the
flow
problem presented above
is
based upon con-
stant
flow
rate.
A
powerful technique called the principle
of
superposition
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~ L L
N A L Y S I S
E S T 7
can be used to treat the more usual variable-rate case by adding together sev-
eral constant-rate cases.
For example, suppose a well is produced at rate q for time t and then
closed for a time interval At. The well pressure, by the principle of superpo-
sition and approximation of variable rate flow into the well, is given by the
following equation:
This is the basic equation for pressure buildup analysis, based upon a
solution for an infinite, homogeneous, one-well reservoir containing a fluid
of small and constant compressibility.
It indicates that a plot of p , versus log (t+Ad/Atshould yield a straight
line. SPE Monograph
1
provides a better understanding and development
of this equation, which is attributed to H ~ r n e r . ~
The theory and practice agree, except for wellbore damage due to skin,
improvement due to fracturing, bounded reservoir, interference due to mul-
tiple wells, phase separation in tubing, fault, etc., as shown on Figure 9-3.
The example buildup curves presented in this figure may be used for some
qualitative interpretation of the actual curves by comparison.
Extrapolation of the straight line to
[ t
+ At>/At] = 1.0 or
log,, [ t+
At>/At] =
0
for infinite shut in time yields a pressure calledp , the “extrapo-
lated pressure” or Horner “false pressure.” For a new well in an infinite reser-
voir,
p
is the initial reservoir pressure,
pi.
f the well is produced for some
time,
p
is approximately equal to the average pressure in the drainage area
around the well.
T Y P E C U R V E
A N A L Y S
I s
The type curve approach is very general, representing the pressure
behavior of reservoirs with specific features, such
as
wellbore storage, skin,
fractures, etc.* Type curves are usually
log-log
plots of dimensionless pres-
sures versus dimensionless times. Each curve corresponds to some dimen-
sionless parameter, characterizing the reservoir and wellbore conditions. The
log scales enhance the visibility of curve variations.
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log [ t+
At)
I
At]
IDEAL SKIN
and/or
WELL FILLUP DEEP PENETRATING
HYDRAULIC FRACTURE
log [ t+ At)
/
At]
log [ t +At) /At]
log
[ t
+At) /At]
BOUNDARY one well in a INTERFERENCE multiple PHASE SEPARATION IN
bounded reservoir)
. .
wells in
a
bounded reservoir)
TUBING
log
[ t
+
At) /At ]
FAULT or NEARBY
BOUNDARY
FromMatthews &
Russell
log
[ t
+At)
/
At]
STRATIFIED LAYERS or
FRACTURES WITH TIGHT
MATRIX
tog [ t+At) /At ]
MOBILITY
LATERA L INCREASE
IN
Fig.
9 3
Example Buildup Curves Illustrating Various Effects
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W E L L A N A LE S T S
I
7
For example, Figure
9 4
represents a type-curve for wellbore storage and
skin effects for the case of a reservoir
wth
infinite extent and homogeneous
behavior. Dimensionless pressure, P,, is plotted versus t,
/ C ,
where each
curve is characterizedby C'e? This figure shows the limits of the various flow
regimes, i.e., end ofstorage and s ta r t of semi-log radial flow, and the rangesof
various well conditions,
i.e.,
damaged, zero skin, acidmd, and fractured.
Dimensionless parameters for wellbore storage and skin in a homoge-
neous infinite reservoir are:
DimensionlessPressare,
Dimensionless
Time,
0 8936C
cD
imensionless
Wellbore Storage Coeflcient,
&thr,2
10
1 z
10
Fig. 9-4 7Jpe
Curve for WeiiboreStorage
and Skin
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Skin Factor,
S
= 0.5 ln(C’P
/ C’
The English field units of the variables are:
k, mD;
h, ft;
4,Bbl/D; p , cp; Ap, psi;
At,
hrs;
c,
(total compressibility), l/psi;
rw,ft;
C
(wellbore storage coefficient), Bbl/psi.
Matching the field data to the appropriate type-curve yields:
The permeability-thickness product, kh, from the pressure match
The wellbore storage constant, C from the time match
The skin fictor,
S,
from the curve match
In addition, the condition of the wellc nbedetermined from the C’@value:
C’P > 1000 Damaged Well
1000
>
C’P >
5 > C’F > 0.5
0.5
> CPS Fractured Well
Normal Well
Acidized Well
Example problem
The interpretation method is illustrated with an example from a training
manual of Scientific Sobare-Intercomp: “Well Test Interpretation in
Practice,” by Gringarten.6The well test consists of a 48-hour drawdown at a
constant rate of 700 bbl/D, followed by a 72-hour buildup. Figure 9-5 pres-
ents pressure vs. time data, and reservoir parameters are given in Table 9-1.
The interpretation process starts with a log-log plot of build-up delta
pressure
vs.
delta time data (Fig. 9-6). Our assumption is that the test well
has wellbore storage and wellbore damage due to skin in a reservoir with
homogeneous behavior. Therefore, buildup data need to be matched with
the type curves of Figure 9-4, corresponding
to C P
greater than 1,000.
An initial type-curve match is shown in Figure 9-7, which indicates a
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~ L L
N A L Y S I SE S T
r,=0.25 f t
h-250f t
tp-48h=
q-700BbWO
0
0
0
0
a
O
Oo c c a p o o o 0 0 8
48 hrs
15
m
Q
lx
vl
a
.-
w”
2
25 50 75
1
000
0
TIME, hours
Fig.
9 5
Pressure Data for Example Problem
~~ ~ ~~
_ _ _ _ _ _ _ _ _ ~
Reservoir Thickness, feet .................... 250
Reservoir Porosity, ...................... . 2 0
Reservoir Permeability, md .................... 6
Total Compressibility, psi-‘. . . . . . . . . . . . . . . . .6x105
Well Radius, foot
........................
.0.25
Reservoir Pressure At = 0 ) , psia
. . . . . . . . . . . . . . 140
Oil Formation Volume Factor, stb/rb . . . . . . . . . . . 1.0
Oil Viscosity, cp . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0
Production Time,
hrs ......................
.48
Production Rate, stb/day .................... 700
Buildup Time, hrs
.........................
. 7 2
10
Table 9 1 Reservoir Data for Example Problem
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104
103
0
v
.-
n
d.
4
102
10
p(Ak0)- 3140
ooo o ~ o o o o o o 000 0
oooo
0
0
0
O 0
0
0
0
0
0
10-2 1
0-1 1
10 102 103
At,
hours
Fig. 9 6 Log -Log Plot of Bu lld-up Pressu re for Examp le Prob lem
reasonable match to C,eZs= lo4,with Horner analysis valid after four hours
(pointer number
2). The purpose of this initial match is to confirm the diag-
nostic of the model and to check if Horner analysis is applicable. It can be
seen that the buildup data do not match the drawdown type-curve after
approximately10hours (pointer number
3).
This indicates that the semi-log
Horner plot is only valid between
4
and
10
hours. Furthermore, the match
id
Fig. 9 7 Init ial Type-Curve Match fo r Example Problem
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~ L L
E S T 7
N L Y S
I
s
indicates the wellbore storage specialized analysis is only applicable to meas-
urements taken before a At of 30 seconds (pointer number 1). Calculations
for
kh,
C, and S are shown in Table 9-2, using Figure 9-6, log Ap versus
log
At
curve, and Figure
9-7,
log
P,
versus log t,/C, curve.
A Horner plot is shown in Figure 9-8. The Horner straight line starts at
4 hours
as
predicted from the type-curve match. It has a slope of 83.2 psi/log
cycle. At [(t, + At)/At] =1 p* = 3756.7 psi. Calculations for flow capacity
Match
results
(CDe2 ),,
= 10
Andpis
results
kh = 141.2q B p P M = 141.2
(700)
1) 1) (.0143)= 1413md-ft
1413 -0.OlbbVpsi
h 1
P TM 1
41.7
= 0.000295--
=
0.000295--
-
c =
-
178.7
8936(0.01)
-- -
8936C
,hi
(0.2)(1.6xl0- )(250)(0.25)* -
1.151 1.151
Homer slope, m
=
805 psiflog cycle
PM -
0.0143
Table9 2
Log Log
Type Curve Calculations for Example Problem
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kh),
effective permeability (k), skin factor
(S),
pressure match,
PM
=
(pJ
m~~~~
( A P ) ~ * ~are shown in Table
9-3.
methods is shown in Table 9-4.
A comparison of the results of the Horner and Log-Log plot analysis
QUESTIONS T O B E A S K E D
B E F O R E
S T A R T I N G A W E L L T E S T
ANALYSIS
Gringarted recommends the following questions to be asked before
starting a well test analysis:
Reservoir questions
1.What type of rock?
If limestone, carbonate, granite, basalt, or loose sand, expect
If acidized carbonate (radius of investigation, ri
fi),
expect
If consolidated sandstone, do not expect double porosity behavior
double porosity behavior
composite behavior
2.
Is this a layered reservoir?
3 . Any known boundary, producing or injecting well nearby,
gas cap, and/or water contact?
w832
3500
p*=3766.7
kb l368 .1 md.ff
91.8
3300
1
Ak
Fig. 9 8 Horner
Plot
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W E L L
T E S T 1
I A N A L Y S I S
Am&s Method
Log-Log Type-Curve
Horner
From Figure 9-8,
my
si/log cycle =
83.2
p*, psi = 3756.7
p At = 1 hr) on Horner
straight
line, psi =
3616.1
k h c s p
1413 0.01 2.0
1368 1.76 3756.7
Analysis results
(700XlXD
kh
=
1 6 2 ~ 5 ~1626-
=
1368.0md-tl
m 83.2
k=-=
3680
5.47
250
S =
1351
48+1
S
= 1.151
S=
1.151
(5.722-7.437+0.009+3.23)=1.76
1351 1351
PM=
0.0138
m
83.2
Table
9 3
Horner Calculations for Example Problem
Table
9-4
Check of Interpretation Consistency
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Well questions
4. Is the well vertical
or
horizontal?
5. What was done to the well?
acidized
fractured
6. Has the well been drilled through the entire formation?
7.
How long has the well been in production?
FluidV questions
8. What is the dominant phase?
oil, gas,or water
9. How many phases?
in the wellbore
in the reservoir
10.
What is the bubble point pressure (oil) or dew point pressure
(condensate
gas)?
expect composite behavior in low permeability reservoirs
(r O fi) if pressure ills below bubble point (oil) or dew
point (condensategas
C H E C K S
D U R I N G
N A L Y S I S
The following checks to be made during the analysis are also from
Gringarten:
Data
validation
1.Check pressure gauges
2.
Check start and end of flow periods
3.Check rate consistency
Model diagnostic
4.
Check time and pressure at start of flow period
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5.
Check derivative smoothing
6.Remember your 10questions
7.Use common sense
Matching and
final
results
8.
Check (P,); n the simulation
Ifdiferent?om Homer Match, keep simzllation
the other parameters
on
Homer Match
9. Check result consistency between flow periods
and regress on
10.
Use common sense
W O R K E N C H W E L L T E S T
A N A L Y S I S S O F T W A R E
The well
test
analysis module of Baker-Hughes/SSI’s Petroleum
WorkBench combines type-curve analysis with conventional semi-log analysis
and can be used to interpret data in homogeneous or heterogeneous forma-
t i o n ~ . ~t contains a variety of near-wellbore, reservoir, and boundary options.
To perform a well test analysis,at aminimum the bllowingdataare needed:
Pressure history data
Production rates for each flow period
Well and reservoir parameters
For test design, the following data are needed:
The model type
Rates for multiple flow periods
Estimates of parameter values
The initial pressure value
Pressure, temperature, and history rate data can be loaded from ASCII
files in any format.
Also
multi-gauge data can be imported and used for a
single analysis. As many
as 50,000
data points can be loaded for each gauge.
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C O
P U T E R A S S S T E D
f E S E R V O I R
d A N A G E M E N T
Well test
analysis
Once the data are specified, WorkBench automatically creates an appro-
priate type-curve, based on user-defined models for near-wellbore, reservoir
behavior, and boundary effects:
Early times-near wellbore effects:
Wellbore storage and skin
Line source solution (for interference tests)
Infinite conductivity or uniform flux vertical fracture
Finite conductivity vertical fracture
Middle times-reservoir behavior:
Homogeneous
Double porosity
Double permeability
Composite reservoir
Composite, double porosity
Late times-boundary effects:
Infinite lateral extent
Single boundary, channel boundaries, open-ended rectangle, rectangle
Partially communicating boundary (leaky fault)
Intersecting boundaries (wedge)
The interpretation process is always started by selecting a flow period to
analyze and identifylng the interpretation model that applies to the data.
There are
two
diagnostic options available for identifling the interpretation
model. They are:
Log-log diagnostic analysis
Horner analysis
Each of these consists of a menu of options within the soha re that
automatically builds a model and generates the corresponding type curve for
matching to the data.
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W E L L TEsr
I
A N A L Y S I
s
A
regression algorithm is available to obtain the best match from the
data. The user can select which variables are to be used in the regression. In
addition, the original data can be either time or pressure shifted to obtain a
better match.
The log-log and dimensionless Horner matches ensure that the analysis is
consistent for the flow period of interest. It is necessary, however, to verify that
the analysis is also valid for the entire pressure history. This is done by simu-
lating the pressure history using the actual rate data, the interpretation model,
the numerical
values
of the well, and reservoir parameters obtained from the
analysis. The computed pressure history can be easily compared to the meas-
ured data. They must match for the analysis to be consistent.
Well test s oha re makes it quite easy to refine the analysis until differ-
ences are resolved and a best fit between the data and a model is obtained.
Then
a
simulated pressure history can be calculated and compared to the
recorded history. Figure
9-9
shows solutions of the previously discussed
example problem.
A
first simulation using results from Horner analysis gives
too high a pressure drop during the drawdown, indicating too low a kh
(1,368md-fi), although the match during the buildup is good.
A
simulation
3 7 0 0 ~
Time match
=
41 7
.g 3500
kh=1428 md ft
h=1404 md fl
--
kh=1368 md ft
_.
g
300
I)n
3100
0
100 200
Time, hours
Fig.
9 9
Simulated Pressu re Histories for Example Problem
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with
kh
=
1 428
md-fi yields too low a pressure drop during drawdown, but
still matches during buildup. A final
kh
value of 1 404 md-fi gives a good
fit
to the entire test.
Well
test
design
Design features of the sohare can be used to generate the expected
rate-normalized pressure response for constant-rate production. The well
and reservoir data, selection of a model, and its correspondingparameter val-
ues are needed. Based on the rate-normalized response, the rate sequence can
be defined and the goals of the design can be reached. Users can display the
log-log and superposition plots for the various flow periods to help them
determine whether the design is adequate. All the plots show both type-
curves and the expected measured data points that will provide the best def-
inition for the test.
A well-conducted test will enable users to reach reasonably confident
conclusions concerning well and reservoir features when the test data are
analyzed. In particular, the analysis of the test quantifies the effects
of:
well damage
hydraulic fracture length
permeability
double-porosity behavior
distance to boundaries
With a poorly designed test, it may be difficult or impossible to analyze
certain reservoir characteristics. For example, the test may be long enough
to show wellbore storage characteristics, but far too short to show distances
to boundaries.
There are several factors that effect the test quality:
Poor data recording or sampling
Inadequate rate information
Insufficient test duration
Infrequent sampling at early times, which may
mask
effects
of hydraulic fractures and other characteristics
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~ L LN A L Y S I S
E S T
The test design
can
clearly show:
whether the sampling
is
frequent enough to show early t i e behavior
how long the test should be within a range of estimated parameter values
R E F E R E N C E S
1.
Mathews, C.
S.,
and Russell, D. G.: Pressure Buildup and Flow Ests in
2.
Earlougher, R.C.: Advances in Well Test Analysis, Monograph Volume
3. Lee, John:
Well
Esting, Society of Petroleum Engineers of AIME,
4. Gringarten, A. C., Bourdet, D. I?, Landet, I? A., Kniazeff,V. J.:
“A
Welh,Monograph Volume 1 SPE, Dallas 1967)
5, SPE, Dallas 1977)
Dallas 1982)
Comparison Between Different Skin and Wellbore Storage Trpe
Curves For Early Time Transient Analysis.” SPE
8205,
paper presented
at the SPE Annual Fall Technical Conference and Exhibition, Dallas,
Texas, Sept. 23-26, 1979
5. Horner, D. R.: “Pressure Buildup in Wells,” Proceedings., Third World
Pet. Congress., E. J. Brill, Leiden 1951) 11, 503
6. Gringarten,A.: Well Est Interpretation Practice, Scientific Sobare-
Intercomp Inc., Denver, CO
7. Petroleum WorkBench: Well Zst
Analysis -
Interpret/2UserManual,
Scientific Sobare-Intercomp, Inc., Denver,
COY
November
2
1
,
1954
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Chapter T E N
PR
D U C T I O N
I
BERFORM NCE
N L Y S I S
The evaluation of the past and pres-
ent performance of a reservoir and the
forecast of its future behavior is an essen-
tial aspect of reservoir management.
Satter and Thakur presented the tech-
niques used for reservoir performance
analyses. Users of production analysis
sohare need to understand the concepts,
theory, and limitations behind the tech-
niques in order to apply them effectively.
O I L A N D G S
R S R V O I R S
Figure
10-1
depicts an example of
hydrocarbon phase behavior,
ie.
pressure
vs. temperature for a given fluid composi-
tion. Reservoir type, whether oil or
gas
depends upon the location of the point
representing the reservoir pressure and
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Temperature
Fig.
10 1
Hydrocarbon Phase Behavior
temperature at the time of discovery For example points
A
B and
C
rep-
resent under-saturated oil bubble point and saturated oil and
gas respec-
tively. Points D,
E
and F represent dry gas dew point gas and condensate
liquid. Figure 10-2 shows under-saturated and saturated oil reservoirs and
also a
gas
reservoir in contact with an aquifer.
N T U R L P RO D UC IN G M E CH N IS M S
Primary performance of oil and gas reservoirs is dictated by natural vis-
cosity gravity and capillary forces. Factors influencing the reservoir perform-
ance are geological characteristics rock and fluid properties fluid flow mech-
anisms and production facilities. The natural producing mechanisms influ-
encing the primary reservoir performance are shown in Figure
10-3.
As can
be noted recovery is dramatically influenced by the type of drive mechanism.
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i
Undersaturated
Oil
Resewoh
aturated
Gas
Reservoir
Fig.
10 2
ydrocarbon Reservoirs
R E S E R V O I R P E R F O R M N C E N L Y S I S
Commonly used reservoir performance analysisheserves evaluation
techniques and the estimates they provide are:
Volumetric
Decline curve
original hydrocarbon in place
reserves
ultim ate recovery
Classical material balance
original hydrocarbon in place
recovery mechanism
Mathematical simulation
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1
f
n
8
I
1
2 3 4 5
6
Reco very Efficiency
OOlP
Fig. 10 3 Eftects of Reservoir Producing Mechanisms on
Recov ery Efficiency
- original hydrocarbon in place
-
reserves
-
ultimate recovery
-
performance under various scenarios
These techniques will be discussed in the following chapters. The accu-
racy of the performance analysis is dictated by the depth
of
understanding
of the reservoir characteristics,
ie.
reservoir model, and the quality of the
techniques used. Table
10-1
presents applicability, accuracy, data require-
ments, and results of the various techniques.
R E S E R V E S
Hydrocarbon reserve is defined
as
the future economically-recoverable
Reserve = Ultimte
Economic
Recovery P a t umuhtive Production
hydrocarbon from a reservoir. Reserve can be defined
as
(Fig. 10-4):
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Applicability/
Accuracy
Exploration
Discovery
Delineation
Development
Production
Data
Requirements
Geometry
Rock
Fluid
Well
Production
Injection
Pressure
Results
Orig. Hydrocarbon
in Place
Ultimate Recovery
Rate vs. Time
Pressure
vs. Time
V
UlU l l lCL l lb
Yes ?
Y e s / ?
Y e s l ?
Yes I Fair
Yes I Fair
Ab
@ S
B
No
No
No
Yes
Yes with
rec. eff.
No
No
Curve Balance Models
No Yes I ? Yes I
No
Y e s l ? Y e s l ?
No
Yes I ? Yes I Fair
o
Yes I Fair Yes I Good
Yes I Fair
Yes
I
Good Yes
I
Very Good
No
A h
A h
No
@A
C
@A C
o PVT VT
No PI for rate locations
Production Yes YeS
homogeneous heterogeneous
vs. time perforations PI
No YeS YeS
o YeS Yes
Yes Yes Yes
Yes Y esw ith PI Yes
No
YeswithPI Yes
Table 10 1 Comparison
of
Reservoir Performance
Anaiysimeserves Estimation Techniques
Reserve is attributed to primary, secondary, or tertiary recovery process-
es. It is changing due to additional production and
also due to any revision
in ultimate production.
Ultimate recovery is given by:
Ultimate Recovery = Original Hydrocarbon In Place
x
Recovery Elfjfciency
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m
a
v
r
o
u
d
t
o
n
r
o
d
u
c
o
n
R
e
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Chapter L E V E N
V O L U M E R I
RETHOD
I N T R O D U T I O N
Estimates of oil and
gas
reserves fall
into three main categories: analogy, volu-
metric, and performance. The method of
analogy is based upon historical experi-
ence from similar reservoirs and wells in
the same area and with similar deposition-
al environment. It is particularly useful
when limited data are available for the
reservoir. The volumetric method for
reserve determination applies after several
wells have been drilled and found to con-
tain hydrocarbons. The performance
methods involve general material balance,
decline curve analysis, and numerical sim-
ulation studies, which are usually done
after gathering data for an extended peri-
od of production.
The volumetric method of comput-
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ing original
oil
or gas in place in a reservoir is usually carried ou t
s
soon
s
sufic ient geological data is available. Th is method is one of the m ost funda-
menta l and elementary tools in assessing the value of a reservoir.
It
is based
upon integration of geological, geophysical, petrophysical, and reservoir data
s listed below:
Field and lease maps
Structure and isopach maps
Open-hole and cased-hole
logs
Core analysis
Porosity and permeability, with cut-offs
Elevations of oil/water and gas/oil contacts
Water saturations , with cut-offs
Basic fluid properties, such s formation volume factor and
solu tion gas-oil-ratio
The volumetric calculations provide
a
reality check on more sophisti-
cated classical material balance and reservoir simulation results.
R S R V O I R
VO LUME
There are two ways of computing basic reservoir volumes. The first
method uses geological structure maps, contoured o n the subsea depths of
both the to p and the base of the reservoir zone in question. The area encom-
passed by each contour is planimetered and plotted on a graph of subsea
depth versus area in acres,
s
shown in Figure
11-1.
Gas-oil and oil-water
contacts are also noted on the plot. T he gross volumes of the gas- and oil-
bearing zones may be determined by integrating the areas under the curves
and accounting for the fluid contacts.
The more frequently used second method involves creating separate
isopach maps for the net oil and/or net gas pay zones, using individual
well-log data. These net sand isopach m aps are developed when sufficient
numbers of wells with data are available for analysis. A general rule of
thum b would be 5-10 wells nicely dispersed) as a minim um num ber for
making a reliable map. T he more wells are available, the be tter the isopach
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VOLUM
T R I C )
I A E T H O D
100 200
300
400 500 600 700 8
rea
in k r e s
e
.Bottom
of
Sand
-Top of Sand
Fig 11 1 Subsea Depth Determining Reservoir Volume
map will reflect the actual reservoir character. Figure 11-2 is an example of
a net pay isopach.
The determination of net pay is complicated by a number of factors:
A
directionally drilled well needs to have
log
measured depths
converted to true vertical thickness, which accounts for the well
deviation s well s the dip and strike of the formation
Location of fluid contacts could differ from well to well s a result
of a
varying transition zone
There must be sufficient effective porosity for commercial production;
in some cases, a porosity cut-off is needed for calculations
Formation permeability must be such that production rates of oil
and/or
g s
are commercially viable
Once the isopach maps have been developed using evenly spaced con-
tour increments, the next step is to compute the available reservoir volume
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Fig. 11 2
Example Net Pay
isopach
in acre-feet. This is usually done by planimetering the area enclosed by each
contour in acres.
A n
example is shown in Figure 11-3, which is a plot of con-
tour area versus contour interval. The volume of the reservoir, 5 may be
found by integrating the area under the curve:
where:
H= total reservoir thickness
A h ) = equation of the curve in Figure 11-3
dh = equals the contour increment
(11-1)
An
analytical expression for the area under the curve would be difficult
to develop. However, this area
can
be determined by graphical integration.
There are two primary means for determination s given below:
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1200
1000
u
g
'
m
, 200
0
0 5 10 15 20 25 30 35 4
lsopach
Contour
feet
Fig. 11-3 Plot of Contour rea vs. Contour Interval
Trapezoidzl Rule
V
=
14H ao 2a, 2a3 + ...
+
2anPI an)+ tn ,
(11-2)
Simpsoni Rule
V
H
a, 4a, Za, 4a3
+
...
+
2an.*
+
4an,
+
an)
+
t,a, 11-3)
where:
V =
eservoir volume in acre-feet
a, =
area enclosed by the zero contour (at oil-water or
gas-oil
contact), acres
al=area enclosed by the first contour, acres
az = area enclosed by the second contour, acres
a, = area enclosed by the nch ontour, acres
t
=
average formation thickness above the top contour, feet
Although Simpson's Rule is a little more tedious and requires an equal
number of contour intervals, it tends to be the more accurate method. The
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smaller the contour interval used, the more closely the true reservoir volume
will be approximated.
Porosity and initial water saturation are determined from log and core
analyses. Formation volume factors are determined by laboratory tests or by
correlation with pressure, temperature, and oil and
gas
gravities. Either a
subsurface sample or a recombination of surface samples from separators and
stock tanks is used in laboratory tests. Note that the fluid formation volume
factors
Bo
and
B)
are needed to convert sub-surface volumes to surface con-
ditions. Their accuracy depends upon how carefully representative samples
of reservoir fluids were obtained and on the validity of the estimates of the
initial reservoir temperature and pressure.
Once the reservoir volume has been determined, the amount of oil and
gas initially in place can be calculated from the relationships given below,
using average rock and fluid properties.
Free
g s
or
g s
cap
With no residual oil present in the
gas
cap,
43,56OV@ I S,)
G =
4
where:
G = in-place gas standard cubic feet
V =
eservoir volume, acre-feet
=
reservoir porosity, faction
S
= connate water saturation
BE
=
gas
formation volume factor, rb/scf
43,560 = cubic feetlacre-foot
Oil in place
With
no free
gas
present in the oil,
(11-4)
7758V@ 1-
S,)
N =
B o
126
(11-5)
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where:
N = oil in place, stock-tank barrels
V =
eservoir volume, acre-feet
@ = reservoir porosity, fraction
S,
= connate water saturation, fraction
B, = oil formation volume factor, rb/stb
7,758 = barreMacre-foot
Solution g s in oil reservoir
G Rs
(11-6)
where:
G
= solution gas in place, standard cubic feet
R = solution gas-oil-ratio, scf/stb
To account for variations in porosity and saturations in the reservoir,
contour maps of thickness
x
porosity
x
initial oil saturation can be prepared.
This will give more accurate estimates of original hydrocarbon in place.
Ultimate recovery can be estimated by multiplying the
OOIP
or OGIP
by the recovery efficiency factor. The oil recovery efficiency factor (STB/acre-
fi or OOIP) and the gas recovery kctor (MMSCF/acre-fi or OGIP) can
be estimated from the performance data from similar or offset reservoirs.
Published
API
correlations
can
be used to estimate primary recovery efficien-
cy for oil
reservoir^. ^.^
For gas reservoirs without the aid of water influx, the
recovery efficiency is usually high, (80-90
OGIP).
Recovery from active
water drive reservoirs can be substantially less, ( 5 0 4 0 OGIP , which is
attributed to high abandonment pressure, g s entrapment, and water coning.
12
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REFERENCES
1.
Atps, J. J.
et al.:
“A
Statistical Study
of
Recovery Efficiency,”
2 Doscher,
T
M., et al.: “StatisticalAnalysis of Crude Oil Recovery
3. Satter,
A.
and Thakur, G . C.: Integrated Petrohm Reservoir Management:
API
Bulletin
D14
1967)
and Recovery Efficiency,”API Bulletin D14 1984)
A Team Approach, PennWell Books, Tulsa, OK 1994)
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Chapter T E
L
vE
D E C L I N E URVE
mCETHOD
When sufficient production data are
available and production is declining, the
past production curves of individual wells,
lease or field can be extended to estimate
future performance Fig. 12-1).There are
two very important assumptions in using
decline curve analysis:
Sufficient production performance
data are available and decline in rate
has been established
All factors that influenced the curve
in the past remain effective through
ou t the producing life
Many factors, such as proration,
changes in production methods,
workovers, well treatments, pipeline dis-
ruptions, and weather and market condi-
tions, influence production rates and, con-
sequently, decline curves. Therefore, care
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Predict Future Production Rates
Forecast Reserves
RATE
Economic Limit
L
TIME
Fig. 12 1 Decline Curve Predictions
must be taken in extrapolating the production curves into the future. When
the shape of a decline curve changes, the cause should be determined, and
its effect upon the reserves evaluated.
DECL INE
C U R V E S
The commonly used decline curves for oil reservoirs are:
Log Production Rate vs. Time (Fig. 12-2)
Production Rate vs. Cumulative Production (Fig. 12-2)
Log of
Water Cut vs. Cumulative Production (Fig. 12-3)
Oil-Water or Gas-Oil Contact vs. Cumulative Production
Log Cumulative Gas Production vs. Cumulative
Oil
Production
When production rate plots (Fig. 12-2) are straight lines, they are called
constant rate or exponential decline curves. Since a straight line can be easi-
ly extrapolated, exponential decline curves are most commonly used. In case
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of harmonic or hyperbolic rate decline, the plots show curvature (Fig.
12-2).
Both the exponential and harmonic decline curves are special cases of hyper-
bolic decline curves. Unrestricted early production from a well shows hyper-
bolic decline rate. However, constant or exponential decline rate may be
reached at a later stage of production.
Water cut curves (Fig.
12-3)
are employed when economic production
a
Log of Production Rate vs. Time
P
Future
q.
.
0 .
......................
.
.C.on9m.i.c
Rate
+ij
.I
Time Life
Production Rate vs. Cumulative Production
I
Cum. Production Prod*
Fig.
12-2
Production Rate Plots
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Cumulative Oil Production
Fig.
12-3
Log
of
Water Cut vs. Cumulative
Oil
Production
rate is dictated by the cost of water disposal. A straight-line extrapolation of
log of water cut vs. cumulative oil production may not be reasonably done
in the higher water cut levels. It may yield a conservative estimate of reserves.
On the other hand, if oil cut data is used instead of water cut in the same
levels, straight-line extrapolation of log of oil cut vs. cumulative oil produc-
tion may deteriorate and lead to overly optimistic reserve estimates.
For gas reservoirs, a p/z vs. Cumulative Production plot gives a straight
line for depletion drive reservoirs, where
p/ z
is the average reservoir pressure
(p)
divided by g s compressibility factore
z)
at that pressure.
D ECL INE C U R V E E Q U A T I O N S
A
general mathematical expression for the rate of decline,
D,
is shown
in Figure
12-4,
where Kand
n
are constants. The type of decline is deter-
mined by the value of n. Standard curve types are:
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Type of Decline n D
Exponential
0 constant
Hyperbolic
O<n<l variable
Harmonic
1 variable
= Kd
q I dt
D=:-
t
Time
Fig. 124
Decline Curve Methods
Eqonentiai
where decline is a constant percentage
Harmonic where decline is proportional to the rate
Hyperbolic where decline is proportional to a fractional power, n, of
the rate; depending on the
n
value, the hyperbolic equation can
model harmonic or exponential decline or something in between
Equations for production rates (4) nd cumulative production Q for
the various types of decline curves'~z re shown below:
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A
general mathematical expression for the rate of decline,
D
can
be
expressed as:
D =
dq dt =
Kq (12-1)
4
where:
q = production rate, barrels per
day
month, or year
t = time, day month, or year
K = constant
n
=
exponent
The decline rate in Equation (12-1)
can
be constant
or
variable with
time, yielding three basic types of production decline
as
follows:
1. ,%ponential or constant decline
(12-2)
D = - - = K = - -q d t
hk
4
t
where:
n =
0,
K =
constant
q,=
initial production rate
qr
= production rate at time t
The rate-time and rate cumulative relationships are given by:
4, q, e-m
12-3)
4.
4
Qt = Y
where:
Q = cumulative production at time t
12-5
A familiar decline rate for exponential decline is
as
follows:
where:
Aq
is the rate change in the first year
In this case the relationship between D nd D s given by:
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I
D E C L r N E
k
U R V EHOD
2. Hyperbolic decline
D = - - -
12-7)
q d t
-
Kq
(0 < n <
I
4
Note that this is the same as the general decline rate equation lz-l),
except for the constraint on n.
Di
or initial condition
K =
the rate-time and rate-cumulative relationships are given
by:
t = q i ( . -k n DitJ-''
12-8)
where:
Di
= initial decline rate
3 . Harmonic decline
D =
-
dq/dt = K g
4
12-9)
(12-10)
where:
n =
1
For initial condition:
K = i
4r
The rate-time and rate-cumulative relationships are given by:
12-11)
(12-1 2)
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A N A L Y S I S
P R O C E D U R E
The general procedure for analysis is as follows:
Gather available production performance data
Observe plots of log of oil rate vs. time, and log of water cut vs.
Determine reasons for production data anomalies (upward or
Evaluate various means of plotting the data
Select the portion of the performance data applicable for analysis
Perform regression for history match
Forecast future performance with economic oil production rate
cumulative production
downward)
and water cut
E X A M P L E
Standard
method
An example is presented to illustrate application of decline curve analy-
sis using Baker-Hughes/SSI's Production Data Analysis program. Figure 12-
Fig. 12-5 Oil, Water, and Gas Production
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r c L r w
C U R V E
M E T H O D
Fig.
12-6
Oil Rate History Match and Prediction
Fig. 12-7 Water Cut History Match and Prediction
13 7
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5
shows oil, water, and
gas
production performance of a well. Figure 12-6
shows performance prediction. The analysis gave exponential decline
of
21.3 per year. Based on an economic production rate limit of 10 STBO
per day the future reserves were calculated to be 19.9 thousand barrels of oil
at year 1990.9. Figure 12-7 is a plot of water cut vs. cumulative oil produc-
tion, which also includes the performance prediction. It shows the well is
expected to exceed 90 water cut before the end of the prediction curve
(which goes to the economic production rate limit). It must be emphasized
that both oil rate and water cut should be analyzed to ensure reliability in the
results. In this example, a water cut limit of 90 would shut the well in, and
the economic rate limit would not be a factor.
Ershaghi
and
Omoregie method
Because extrapolation of the past water cut plot is often complicated,
Ershaghi and Omoregie3devised
a
method to plot recovery efficiency vs. X
as
defined below, which yielded a straight line:
ER=
mX
YZ (12-13)
where:
ER=
over-all recovery efficiency
X n(l/f, - 1)
-l/”
(12- 14)
f = fraction of water flowing
m
= slope
n
= constant
This method is claimed to be more general than the classical plot of water
cut
vs.
cumulative oil production, and more applicable when water cut
exceeds
0.5.
Given actual water cut vs. recovery efficiency data, a graph of
recovery vs. Xwould result in a straight line, which may be extrapolated to
any desired water cut to obtain corresponding recovery. The parameters
m
and
n
in Equation (12-13) can be derived from the straight-line relationship
in Figure 12-8. These values then can be used in Equation (12-13) to predict
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0.15
0.13
g
0 11
-3
c€i 0.09
c
d
8
0 07
0.05
0.03
0 01
2.00 2.20 2.40 2.60 2.80
3.00
3.20
3.40
3.60 3.80
4.00
X =
[ In x, 1 - z,
1
Fig. 12 8 Recovery vs.
X
1
o
0 8
0 2
0
0 0.03
0.06
0.09
0.12
Recovery Fraction
0 15
Fig. 12 9 Recovery vs. Water Cut
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E S E R
V O I R
f’h
“A G E M E N T
P U T E R A S S S T E D
water cut vs. oil recovery in Figure 12-9, which shows clearly
that
straight-line
extrapolation of water cut would result in pessimistic ultimate recovery.
R E F E R E N C E S
1. Arps, J. J.: “Estimation of Decline Curves,”Trans. AIME 160
2. Arps,
J.
J.:
“Estimation of Primary
Oil
Reserves,” Trans.
AIME
207
3. Ershaghi, I. and Omoregie,
0 “A
Method for Extrapolation of Cut
1945): 228-247
1956): 182-194
vs. Recovery Curves”,
JPT
Feb. 1978) 203-204
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Chapter
T
I
R T E E N
M T E R I B L N C E
L
k E T H O D
The classical material balance method
is more fundamental than the decline
curve technique for analyzing reservoir
production performance. This method is
used to estimate the original hydrocarbon
in place oil or
gas ,
and ultimate primary
recovery from a reservoir. It is based upon
the law of conservation of mass, which
simply means that fluids are conserved,
i.e.,
neither created nor destroyed. The basic
assumptions made in this technique are:
Homogeneous tank model,
i.e.,
rock and fluid properties are the
same throughout the reservoir
Fluid production and injection
occur at single production and
single injection points.
There is no direction to fluid flow
However, in reality the reservoirs are
not homogeneous, production and injec-
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tion wells are areally distributed and activated at different times, and fluid
flows in definite directions. Nevertheless, the material balance method is
widely used and it is found to be a very valuable tool for reservoir analysis,
with reasonably acceptable results.
The material balance equations for oil and
gas
reservoirs are used for the
following:
history match of past performance
estimating original hydrocarbons in place
prediction of future performance
Satter and Thakur presented general material balance equations for oil
and
gas
reservoirs, and discussed their
application^^ ^ ^^^ ‘^^
O IL R S R V O I R S
General material balance for oil reservoirs can be expressed
as
the equa-
tion of a straight line:
F
= N Eo mEg Efi
y
13-1)
where:
F
= production of oil, water, and gas rb
N
=
original oil-in-place, stb
E
=
expansion of oil and original
gas
in solution, rb/stb
m =
initial
g s
cap volume, fraction
of
initial oil volume
g
= gas
cap expansion, rb/stb
Efi=
connate water expansion and pore volume reduction d ue to
y
umulative natural water influx, rb
The above equa tion con tains three unknow ns: original oil in place,
gas
cap size, and natural water influx. The unknown parameters can be deter-
mined by history matching for different drive mechanisms:
production, rb/stb
Solution
gas
drive oil reservoirs-unknown is the original oil in place
Gas cap
drive
oil reservoirs-unknowns are original oil in place an d
gas cap size
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MA T E R I A L A L A N C E )
MBETH D
Water
drive
oil reservoirs-unknowns are original oil-in-place and
natural water influx
There are five commonly used graphical methods2,3,4,5,Gased on the
material balance straight-line equation:
1.
FE method-plot of total production vs. total expansion
2. Gas
cap method-plot of total production/oil expansion vs.
gas
3.
Havlena-Odeh method-total production/total expansion vs. water
4. Campbell method-total production/total expansion vs. total
5. Pressure method-pressure vs. oil production
A more detailed discussion of the above methods may be found in
expansion/oil expansion
influxhotal expansion
production
reference
1.
Reservoir data required for history match are:
1. cumulative gas, oil, and water production from the reservoir for
2.
average reservoir pressures at the corresponding time “points” of
1)
3.
PVT data for the reservoir fluids over the expected reservoir pressure
a series of time “points”
ranges
If
the original oil in place, gas cap size and aquifer strength and size are
known, material balance equations can be used to predict the future per-
formance. However, solutions for gas
cap
drive and natural water drive reser-
voirs are very complex. Performance prediction above the bubble point is
rather straightforward. However, prediction below the bubble point requires
simultaneous solutions of material balance equation and subsidiary liquid
saturation, produced gas-oil ratio, and cumulative gas production.
G A S
R S R V O I R S
General material balance
as
an equation of straight line for gas reservoirs
c n be expressed
as:
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1
3-2)
=
5
E )
where:
G =
original gas-in-place, scf
F =
Production of
gas
and water, rb
5 = Expansion of gas, rb/scf
E = Connate water expansion and pore volume reduction, rb/scf
= cumulative natural water influx, rb
The unknown original
gas
in place, and natural water influx parameters
in the above equation can be determined by history matching for depletion
drive and water drive reservoirs. There are four commonly used graphical
methods to calculate original
gas
in pla~ e:** ~”~~
1.
p/z method-reservoir pressure/z-fictorvs. cumulativegas production
2. Cole method-total production/total expansion vs. total production
3.
Havlena and Odeh method-total production/total
gas
expansion
4.
Pressure method-reservoir pressure vs. cumulative
gas
production
Data required for history match are:
1.
cumulative gas, condensate, and water productions from the
2.
average reservoir pressures at the corresponding time “points” of
1)
3.
PVT data for the reservoir fluids over the expected reservoir pressure
vs. water infldtotal expansion
reservoir for a series of time “points”
ranges
The future performance of
gas
reservoirs without water influx and water
production can be calculated directly from a material balance equation giv-
ing a straight-line relationship between p/z and cumulative gas production.
Data needed for calculating
gas
production are: gas compressibility 2) vs.
pressure p) and original
gas
in place. Performance prediction for a water
drive gas reservoir is more complex.
MATERIAL
B A L A NC E
E X A M P L E ~ A S E TUDY
Satter and Thakur’ reported computer analyses of several
oil
and
gas
reservoir case studies. Readers
are
referred to that book for details of these
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I ATER I A L ALANCE
MBETHOD
studies. The oil reservoir cases presented were an undersaturated reservoir, a
gas
cap drive reservoir, and a natural water drive reservoir with a small gas
cap. The gas reservoir cases presented were a depletion drive Gulf Coast
reservoir, an abnormally pressured reservoir, and a reservoir with an infinite
linear aquifer.
An
example of a material balance study carried out on a Middle East oil
reservoir is presented in this chapter. The reservoir is Devonian in age and
has the following general characteristics:
Reservoir was discovered and placed on production in the early
1960s
Approximately
30
years of production and pressure history were available
Reservoir depth = approximately7,700
fi
Initial temperature=
200’
F
Reservoir was saturated, with an initial pressure and bubble point
of approximately
3,200
psia
Oil
gravity
=
approximately
42” API
GOR
=
approximately
1,100
STB/SCF
Connate water saturation = 12
Residual oil saturation estimated at
25
Figure
13-1
shows historical pressure data available over a 30-year peri-
od. Average reservoir pressure was clearly declining for almost
20
years when
an apparent re-pressuring started to occur, possibly due to delayed water
influx into the reservoir. There was no record of water injection in this reser-
voir or in any other reservoir where the completed wells had penetrated.
There were also no signs
of
any communication behind pipe, which could
have accounted for the average reservoir pressure starting to increase slowly.
Figures
13-2
and
13-3
show performance plots for daily oil, water, and gas
production rates and water-cut and GOR, respectively.
The initial reservoir description, based upon well log analysis, showed a
reservoir with an apparently large
gas
cap and a thin oil rim, with a large
aquifer present. A decision was made to carry out a material balance study
as
part of an overall evaluation. MBAL, a general material balance program,
from Petroleum Experts was the software package that was used in this
analysis. For other general material balance software packages available on
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Pressure
vs.
Time
I I I I I I I I I I I I I I I l I I I 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 l 1 1 1 1 1 1 1 1 1 1
4 L :
fl i ;
1
r
I 7 r
7 r
I I
7
I z I
r
-I- 7 r I r I-
1 1 1 1
I I I I I I I I I I I I I I I I I I I I I I I
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 l 1 1 1 1 1 1 1 1 l I I I I I I I I I I I I I I
I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 l l l I I I I I I l l l l l l l
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
6 0
Ju n 65 Jun-70 Jun-75 Jun-80 Jun.85 Jun-90
30,000
Fig
13 1
Pressure Data for Case Study
Fluid Production Rates
35,OOt
30,000
5 , 25,OOC
p
B
p
20,000
0
15,000
5,000
I I I I I I I I I
I I I I I I
20,000
I
15,000
10,000
5,000
0
Ju nk 0 Jun-65 Jun-70 Jun-75 Jun-80 Ju n-85 Jun -90
+Oil Rate Water Rate
-Gas Rate
Fig
13 2
Production Rates for Case Study
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the market, see chapter
15 .
The purpose of this material balance study was
to obtain initial oil-in-place and the ratio of gas cap volume to oil volume.
In addition, the relative strength of the aquifer would be determined.
Once all production data and pressure history were loaded into the
MB L program, regression calculations were carried out that varied param-
eters such as
aquifer model
aquifer system defines the flow prevailing in the reservoir
and aquifer system, such as bottom water and edge water)
aquifer boundary condition constant pressure, infinite
acting, or a sealed)
reservoir equivalent radius
aquifer permeability
aquifer volume
The regression was carried out until an acceptable match was obtained
Water Cu t Gas-Oil-Ratio
vs.
Time
100
8
6
5 000
4 000
P
3 000
v
v
p
2 000
3
z
2
1 000
0 0
JunsO Jun-65 Jun-70 Jun-75 Jun-80 Jun-85 Jun-90
-Water Cut
-o-GOR
Fig. 13 3 Water Cut
and GOR
for Case Study
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to the pressure history.
MBAL
also allowed the regression “best fit” to be
compared with other traditional material balance methods such
as
Havelena
and Odeh and others. Figure 13-4 is a drive mechanism plot w hich shows
how this reservoir started out with a dominant gas cap expansion.
Approximately halfivay through the 30+ year producing period, water influx
became the dom inant drive mechanism to the extent that at the end of his-
tory the average reservoir pressure had begun to slightly increase. There had
been no reported
gas
or w ater injection for pressure maintenance. Figure
13-
5
shows the result of regression history with pressure. The “best
fit”
regres-
sion gave the following results:
Aquifer model Hurst-Van Everdingen-Dake
Aquifer system Bottom water drive
Boundary model Constant pressure boundary
Aquifer volume
04,448 MM ftj
Aquifer permeability
375
md
Equivalent tank radius
,500fi.
Drive Mc h d s r
1
0 75
0 . 5
0.25
06/15/1961 12/14/1968 14/ 9 12/14/1983 06/15/1991
im daten/d/y)
Fig. 13 4 Drive Mechanism Plot
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9 ’ ” ’ ”’
” ”
TsnL
Rudrua
449.706 1 1
Oil 1
Place
S1 . 64U (lIS83)
P t O d l l c U i o D
W C
06/1S/1910 I4aW S Qfp
Fig 13 5 Regression on Pressure History
Initial oil in place 360 MMSTB oil
Gas cap/oil volume ratio 1.78 1
RE FERENCES
1. Satter, A., and Thakur, G. C.: Integrated PetroZeum Reservoir Management:
2. Havlena, D., and O deh, A.
S.:
“The Material Balance as an Equation of
3. Havlena, D., and Odeh, A.
S.:
“The Material Balance
as
an Equation of
4. Wang, B., and Teasdale,T. S.: “GASMAT-PC: A Microcomputer
A Team Approach, PennWell Books, Tulsa, Oklahoma (1994)
Straight Line,” J. Pet. Tech. (August 1963) 896-900
Straight Line-Part 11, Field Cases.” J. Pet. Tech. (July 1964) 815-822
Program for Gas Material Balance with W ater Influx,” SPE Paper
16484, Petroleum Industry Applications of Microcom puters, Del Lago
on Lake Conroe, Texas, June 23-26, 1987
149
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5.
Campbell,
R
A., and Campbell,
J.
M.,
Sr.: Mineral Property Economics,
Vol.
3: Petroleum Property Evaluation, Campbell Petroleum Series
1978)
6. Schilthuis,
R .:
“Active Oil and Reservoir Energy,” Trans. AIM E
7. Cole,
F.
W.: Reservoir Engineering Manual, Gulf Publishing Company
1936) 118, 33-52
1969)
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RESER
O I R 3
sYImuLA
T I O N
R S
ERVO I
R
S I
MU L A T
I ON
Reservoir simulators are widely used to
study reservoir performance and to deter-
mine methods for enhancing the ultimate
recovery of hydrocarbons from the reser-
voir. They play
a
very important role in the
modern reservoir management process,
and are used to develop a reservoir man-
agement plan and
to
monitor and evaluate
reservoir performance during the life of the
reservoir, which begins with exploration
leading to discovery, followed by delin-
eation, development, production, and
finally abandonment. Satter and Thakur
provide an overview of reservoir simulation
concepts, and processes.
Concept and well management
the following concepts:
Num erical simulation is based upon
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Material balance principles
Reservoir heterogeneity
Direction of flow
Flow of phases
Spatial distribution of wells
Unlike the classical material-balance approach, a reservoir simulator
incorporates details of well management:
Location of production and injection wells
Completions
Rate or bottom hole pressure specified, or even both
The wells can be turned on or off at desired times with specified down-
hole completions. The well rates and/or
the
bottom hole pressures can be
set
as
desired.
Technology
and
spatial
and
time discretization
Simulation has become a reality because of advances made in comput-
ing power and the technology now available for data handling, computa-
tional techniques, report writing, and graphics. These allow the reservoir to
be divided into many small tanks, cells, or blocks to take into account het-
erogeneity (Fig. 14-1). Computations of pressures and saturations for each
cell are carried out at discrete time steps, starting with the initial time.
Phase and direction
of
flow
Black oil simulators are characterized by the number of (fluid) phases,
directions of flow, and the type of solution used for the complex fluid flow
equations. The fluid phase can be:
single phase (oil or gas
two
phase (oil and
gas,
or oil and water)
three phase (oil, gas, and water)
The direction of flow
c n
be:
1-Dimensional linear or radial, when only in one direction
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rfzl
ank
m
-D
1-D Radial
Cross-Sectional
Areal
Radial
Cross-Sectional
Fig. 14 1 vp ical Simulation Models
3 D
2-Dimensional areal, cross-sectional, or radial cross-sectional when
3-Dimensional when in
x-y-z
direction
in x-y, x-z, or r-z directions
Fluid flow equations
Simultaneous flow
of
oil, gas, and water through a porous medium is
described
by
a
set
of
three (oil,
gas,
water) complex
partial
differential equations:
Law of conservationofmass
Fluid flow law (Darcy s)
PVT behavior
of
fluids
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I
C O ~ P U T E R S S ~ S T E D
E S E R V O I R
A N A G E M E N T
These flow equations contain
six
unknowns:
where
S
and
p
denote saturation and pressure, respectively, and sub-
Auxiliary relationships involving saturations and capillary pressures
scripts
0 g
and
w
denote oil, gas and water.
must be used in order to solve the fluid flow equations:
so+
g
=
1
PEMvPo-P, =PmW SbS,
q
P - Po
=
Pq Sb
s
where
p,,
and
pq
denote capillary pressure between oil-water and oil-gas,
respectively. If the capillary pressure is zero, there is only one pressure,
s
the
number of unknowns reduces t pressure, and oil,
gas,
and water saturations.
Approximate solutions of the complex equations c n be obtained by
using finite difference schemes. The pressures and saturations
c n
be solved
explicitly, implicitly, or by a combination method:
Fxplicit-explicit pressure, explicit saturation
Implicit-implicit pressure, implicit saturation
Combination-implicit pressure, explicit saturation ( I M P S )
RESERVOIR S I M U L T O R S
Reservoir simulators are generally classified in four categories governed
by flow mechanisms:
Bbck Oil:
Fluid flow
Compositional: Fluid flow
Phase composition
TbermuL:
Fluid flow
Heat flow
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Chemical* Fluid
flow
Mass transport due to dispersion, adsorption,
and partitioning
Various stand-alone and integrated s o h a r e packages for black oil, com-
positional, and thermal models are reported in the next chapter.
S I M U L T I O N
R O C E S S
In general, the reservoir simulation process (Fig.
14-2)
can be divided
into
three main
phases:
Choose the
Right Model
8
and geoscience
data
Avoid arbitrariness
in
history-matching
.Apply common
sense
Apply various scenarios
ocumentation
Fig. 14-2 Reservoir Simulation After
SPE
paper 18305 by
Saleri and Toronyl)
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Input Data Gathering
-
geological, reservoir, well com pletions,
production, injection, etc.
History Matching
- initialization, pressure match, saturation match,
and productivity index match
P 4 o m n c e Prediction
-
existing operating and/o r som e alternative
development plan
Input data consists of general data, grid data, rock and fluid data, pro-
duction/injection data, and well data. Gathering the needed data can be very
time consuming and expensive. Ascertaining the reliability of the available
data is vital for successful reservoir modeling.
History m atching of the past production and pressure performance con-
sists of adjusting the reservoir parameters of a m odel until the simulated per-
formance matches the observed or historical behavior. This is a necessary step
before the p rediction phase because the accuracy of a prediction can be no
better than the accuracy of the history match. However,
it
must be recog-
nized that history matches are not unique.
The stepwise history matching procedure consists
of:
productivity matching (Fig.14-5
pressure match ing (Fig. 14-3 followed by
saturation matching (Fig. 14-4 followed by
Predicting future performance of a reservoir under existing operating
conditions and/or some alternate development plan such as infill drilling,
waterflood after primary, etc., is the final phase of a reservoir simulation
study. Th e m ain objective is to determine the optim um operating condition
in order
to
maximize economic recovery of hydrocarbons from the reservoir.
B U S E
OF RESESRVOIR
S I M U L A T I O N
The use of reservoir simulation has grown steadily over the last
25
years
because of the constant improvement in simulator software and computer
hardware. The rapid growth and acceptance of simulation has led to some
confusion and occasional misuse of the reservoir engineering tool because of
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I
R E S € p I R
I M U L A T I O N
ct = TOTAL COM PRESSIBILITY
CP =POROSITY
k =
PERMEABILITY
h
=THICKNESS
We
=
WATER INFLUX FROM AQUIFER
RATES
s)
/al
SIMULATOR
CRECK RUN
ADJUST RAT E
CONSTRAINTS
ADJUST
ADJUST
, , @ , h , w ,
5
SATURATION MATCH
Fig. 14-3 Pressure Match Procedure
unrealistic expectations, insufficient justification for simulation, and unreal-
istic reservoir description.
G O L D E N R U L E S
F O R
SIMUL TION
E N G I N E E R S
Recognizing that there is increasing danger that inexperienced users will
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PRESSURE MATCH
1
k,
=
RELAT IVE PERMEABILITY
Pc
=
CAPILLARY PRESSUR E
IMULATOR
CHECK RUN
ADJUST
CONTACTS
LOCALk,P,
I
9
PRODUCTIVITY MATCH
Fig.
14-4
Saturation Match Procedure
misuse sophisticated models available to them, Aziz3 offered the following
basic rules in order to minimize this danger:
1.
2.
3.
4.
158
Understand your problem and define your objectives.
Keep it simple. Start and end w ith the simplest model.
Understand model limitations and capabilities.
Understand the interaction between different parts of the model.
Reservoir, aquifer, wells, and facilities are interrelated.
Don’t assume bigger is always better. Always question the size of a
study that is limited by the com puter resources and/or the budget.
Quality and quan tity of
data
are important.
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PRESSURE. AND
SATURATION MATCH
1
ADJUST
WELL
PRODUCTIVITY:
PI,
WI
ADIAL FLOW,
BACK PRESSURE
HISTORY
MATCH RUN
PREDICTIONS
Fig.
14-5
Productivity Match Procedure
5.
Know your limitations and trust your judgment. Remember that
simulation is not an exact science. Do simple material balance
calculations to check simulation results.
6 .
Be reasonable in your expectations. Ofien the most you can get
from a study is some guidance on the relative merits of choices
available to you.
7. Question data adjustments for history matching. Remember that
this process does not have a unique solution.
8.
Never smooth or average out extremes.
9.
Pay attention to the measurements and the scales at which they
were made. Measured values at the core scale may not directly apply
at the larger block scale, but measurements at one scale do
influence values at other scales.
10. Don t skimp on necessary laboratory data. Plan laboratory work
with its end use in mind.
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E X M P L E S
Applications
of
Baker-Hughes/SSI’s Black Oil Simulator are presented
for:
Newly discovered field development plan in chapter 16
North Apoi/Funiwa field optimization study in chapter 17
Waterflood project development in chapter 18
R E F E R E N C E S
1 Satter, A. and Thakur, G. C.: Integrated P etrohm Reservoir Management:
2. Petroleum WorkBencb Reference Manuah, Scientific Sohare Intercomp,
3.
Azii
Khalid: “Ten Golden Rules
for
Simulation Engineers,” Dialog,
JPT,
A Team Approach, PennWell Books,Tulsa, Oklahoma 1 994)
Denver, Colorado
Nov., 1989
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C O N P U ~ E R
I
O F T W A R E
In the early days of computerization,
most of the software available to the geo-
scientist or engineer consisted of main-
frame applications that ran in batch
mode. The nature of these programs gen-
erally restricted their use to a few special-
ists at the larger companies. Over the
years, the evolution of both hardware and
software technology has provided many
new tools for improving the reservoir
management process. Speed of opera-
tion, data capacity, and communication
capability, which have improved expo-
nentially while costs have decreased, have
brought personal computers or worksta-
tions to nearly every desk. Operating sys-
tems and the applications themselves
have become easier to use, with emphasis
on reaching a wide variety of users, not
just the specialist.
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P U T E R - A S S
S T E D
E S E R
V O
R
h N A G E M E N T
Many of the major oil companies wro te their own software in the past,
seeking a competitive advantage for their p roprietary techniques. Although
this is still done to some exten t, companies now have the need of so many
applications that
it
is not cost effective to create them all in-house and to
provide support to the growing num ber of users. Th is has led to the growth
of a large service industry, which develops and markets software throughout
the oil and
gas
business.
S T A N D A L O N E S O F T W A R E
Many com puter applications have been developed for specific purposes.
It is faster and m ore econom ic to produce a program to focus on a specific
task. Such programs are often easy to use and relatively inexpensive. T he
simplest of these stand-alone programs may still be written by the end-user,
or at least within the user’s company (such
as
a spreadsheet to do a specific
type of log analysis). More complex applications come from the service com -
panies (which may also offer consulting services based on their
own
use of
the software) and software specialty companies. Table
15-1
lists a few of the
many stand-alone computer programs currently on the market, which play
an im portant role in our reservoir management process. Nearly all of the list-
ed software programs are either Windows- or Unix-based applications. Th is
list is by no means intended to be all-inclusive, bu t it is representative of the
types
of stand-alone software packages that are available today.
INTEGR TEDO F T W A R E P A C K A G E S
As
more and more reservoir management tasks have become computer-
ized, the need has grown to use the results of one program
as
inpu t to anoth-
er. Several vendors have developed “integrated packages” by com bining sev-
eral applications in such a way that data can be easily moved from one pro-
gram to another. All programs within one package generally have a similar
look and feel,
or
common user interface, making it easier for one user to
operate all of them. To date, few such packages (if any) have been developed
as a
cohesive unit. Instead, they have been created by patching together sev-
eral previously existing stand-alone applications. There are several service
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Vendor Program
D.P.COOK OGRE
EPS PANSYSTEMS
ESP
WELLFLO Nodal analysis and g s lift design and optimization
as
pipeline wellbore and reservoir deliverabilitymodel
EKETE PIPER
Description
Economic evaluationprograa~
Well
t st
design and pressuretransientanalysis
Submersiblepump design and analysis
WELLTEST ressure
tr nsient
analysis
VALIDATA
Read
pressure and
temperature ata
from electronic
FIELDNOTES
SIMTRAN
SIMFRAC
TERASIM
SIMPERF
recorder
Transfer well production est datafrom ield to
office
Numerical well
test
analysis
Design
of
propped and acid hc tu rin g treatments
A d v a n d reservoir simulation oftwate
Near w ellbore simulatorto optimize perforation
perform nce
GEOSTAT
Transfer fine-scale geost tistic l resultsinto reservoir
Table 15 1 Stand alone Software Packages
companies that have a first-generation set of integrated reservoir manage-
ment software. Table 15-2 summarizes these various applications. Again this
is not intended to be a complete list.
Most service companies originated around a fairly narrow range
o
expertise and have not had the capability to create truly all-encompassing
integrated packages. This has resulted in numerous mergers o companies
whose strengths complimented each other.
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GEOSCIENCE
Table 75 2 htegrated Software Packages
P E T R O T E C H N I C L O P N
S O F T W A R E C O R P O R A T I O N
Data management has become one of the greatest difficulties in using
this vast array of software tools available today. It is usually convenient to
move data among the various programs within one integrated package or
even am ong the stand-alone programs from one supplier. There has been
limited standardization throughout the industry. This has m ade it very dif-
ficult for users to retrieve data from their company database (if they have
one a t all) and inp ut i t to various applications from different vendors.
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Several years ago,
a
group consisting of most of the oil and
gas
compa-
nies and service companies go t together to discuss this problem, which
they all shared. The result was the formation of Petrotechnical O pe n
Software Corporation (POSC ), for the purpose of defining data storage
and software standards for the entire industry.
Some progress has been made by POS C, bu t the legacy of existing
software and databases within the m ajor oil com panies and service compa-
nies alike has hampered agreement and development of true standards.
Hopefully, time will solve this problem, as older software becomes obsolete
and needs to be replaced.
In the meantime, geoscientists and engineers c n take advantage of the
tools now available to gain a more thorough understanding of their reser-
voirs and to develop optimum plans for managing those assets.
COMPUTING
FAC I LIT
I
E S
Computing facilities in the petroleum industry have changed greatly
over the past few years. As hardware has become more powerful and less
expensive, central mainframe computers have been replaced by personal
computers and networked workstations. One modern example is the
Reservoir Computing Facility
at
Texaco Exploration Production
Technology Departmen t (E PT D), illustrated in Figure 15-1.These comput-
ers are often used by E PTD staff to run projects for Texaco’s business units.
Th ey can also be accessed from the remote division offices over the netw ork.
There are many software applications installed at EPT D in order to pro-
vide com patibility with the division ofices and outside project partners. T he
principle applications are shown in Table 15-3.
INT RN T
Use of the
WWW
(World W ide Web) has exploded in the petroleum
industry, with the service companies as well as many of the major oil com-
panies setting up their own public web sites. For the vendors and service
companies, these web sites supply a vast wealth of information such as:
company mission statements
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RlSC
6
Cluster
Resewot Simulation WaW ati on
JEX CQ
Resewoi
Characterization
3D VisualizaticnWorkstation
Fig 75-7
exaco
EPTD Comput ing Environm ent
sales and marketing information
demonstration versions of s o h a r e programs
service packs to patch and/or upgrade current versions of the sofcware
technical bulletins and product information
phone numbers and e-mail addresses for support and sales contacts
Table 15 4 lists the
U U
addresses of several companies that have post-
ed information about their products and organization on the Internet. A
more complete listing of energy related links may be found at the web site
of
the Petroleum Technology Transfer Center-www.pttc.
org/Linkinh
htm
Many companies are increasingly using private registration-only extranets
often on trusted servers to provide secure real-time project management
service and communication with their customers.
Internet search engines such s www.yahoo.com can be used to expand
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a
?
t
Laudmark
akerHughd omputer Unhrblayr eoQuaf
Smedvig
Modd
Type SSI Moddiug ofTexas
Reservoir
Group Technologies
Blac koi l VIP ENCORE SIMBEST II
I M X ECLIPSE
MORE
Compositional VIP COW C O W I n G M UTCOMP ECLIPSE MORE
Thermal VIP THERM THERM STARS
U ITHERM
Chemical UTCHEM
Miscible VIP ENCORE SIMBEST I1 IMEX
1
3
PVT DESKTOP PVT PVT CMGPROP PVT
PR EXEC HM VFPI
spost HVWELL EDIT
GRIDGFNR GRID
Proc xwrs
LGR
LGRJGC
FILL
PLOTVIEW XYPLOT GRAF
3DVIEW RESULTS RTVIEW
Inkgrated DESKTOP PVT WORKBENCH
Applications
GEOLINK
Reservoir SIGMAVIEW
Charactnrratt on SGM
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E S E R V O I R V M A N A G E M E N T
P U T E R - A s s S T E D
PGS
PIDwight’s
m c ist ofLnks
SchtUmbergrr
ScientificSoffware-Intercomp
ROX R
SPE
ulfcoast
Section
SPE
International
sp rry
sun
U.S.G.S.hom page
Uuivemity ofTexas
Societyof Prof. Well
Log
nalysts
www.pgs.com
www.ihsenergy.com
www.pttc.or@ hdx.htm
www.wmect.slb.com
www.ssii.com
www.smedtech.com
~ . s p e g c s . o r g
www spe Org
www.sperry-suxt.com
www info er usgs gov
WWW.pt.utexss.L?dU
www.spwla.mg
Noto:
URLs
ometimes change
Table 15-4
Energy-Related
Web
Sites
this initial list and to pinpoint specific needs. The following are brief
excerpts of information taken from the Internet about various companies
and groups. They represent just a very small sample of the inform ation avail-
able from the Internet. These excerpts are illustrated by figures copied from
the associated home pages:
Baker
Hughes (SSI)
Figure 15-2
Baker Atlas Geoscience subsidiary Scientific Sohare- In te rcom p devel-
ops and markets s o h a r e for development and production, for pipeline and
surface facilities, and provides associated interdisc iplinary technical support
services, consulting, and training. O ne of its principal s o h a r e products is
Petroleum WorkBench.
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Fig.
15-2
Baker Hughes SSI)
1
Fig. 15-3 KAPPA Engineering
KAPPA
Engineering
Figure 15-3
KAPPA Engineering is a software and consulting company. Primary
software products are
SAPHIR for
pressure transient analysis and EMER-
AUDE for production logging.
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W 2
Reservoir Characterization Research and Consu lting, (RC)*, deals with
the integration of diverse
data
types for petroleum reservoir modeling and
provides
a
wide-range of software tools for reservoir description as well
as
geostatistical m odeling.
Fekete Associates
Figure 15-4
Fekete Associates is a Canadian petroleum engineering consulting firm
that has software tools for well pressure transient analysis, economics, gas
gathering systems, and production engineering.
Smedvig Technologies
Smedvig Technologies is a service company that provides multi-disci-
pline sofnvare in the areas of basin modeling, petrophysics, gridding, reser-
voir simulation, and drilling, and has merged with M ulti-Fluid.
CMG
Figure
15 5
black-oil, com positional, and thermal reservoirs.
Computer Modeling G roup provides software for reservoir simulation of
Fig. 15-4 Fekete Associates
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Beicip-Franlab Figure
15-6
Beicip-Franlab is a French-based service company
that
offers software
for basin analysis reservoir characterization reservoir simulation and
3 D
model building.
Welcome to
Computer
~ o d e l i i ~ group Ltd.
Fig. 15-5 Computer Modeling Group CMG)
Fig. 15-6 Beicip-Franlab
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Landmark
Graphics Figure
15 7
Landm ark Graphics, a H alliburton Company, provides integrated soft
ware for seismic interpretation, reservoir characterization, basin modeling,
and reservoir simulation. Key products include SeisWorks, PetroWorks,
Z
Map Plus, StrataModel, Desktop VIP nd D View.
U. S.
Department
of
Energy Figure
15 8
public domain an d available from the federal governm ent.
Merak Figure
15 9
Merak develops and markets WindowsTM pplication software for the
upstream energy industry. Merak s suite of integ rated tools assists in the eval-
uation of exploration risk, production forecasts, economic projections,
investment viability, and production operations. Features include mapping,
budgeting, regime-specific financial projections, rig scheduling, wellbore
schematics, reserves, and production data management.
Th is is an example of reservoir management software developed in the
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U.S.
Departmento
Energy
Computer Software
and Supporting I ocnmentatlon
h o omplIi=rPmg au
Fig 15 8
US
Department of Energy
E
Fjg
15 9
Merak
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SP Gulf
Coast Section Figure
15-10
This is the home page of the Society of Petroleum Engineers’
Gulf
Coast Section located in Houston, Texas. The web site offers a career man-
agement section, on-line event registration, and other services for its mem-
bers and other energy professionals such as an in-depth group of energy
related web links.
SP International Figure
15-1
1
This is the home page of the Society
of
Petroleum Engineers
International. The web site offers on-line information, membership directo-
ry, industry directory with corporate links, technical paper search, plus links
to local sections
as
well as an Internet Career Center.
Halliburton Figure 15-12
Halliburton is a major oil field service company offering a ul l range of serv-
ices to the energy industry. They are a world leader in energy equipment, ener-
gy
services, engineering, and construction. Their goal is to provide their
cus-
tomerswith solutions that increase oil and gas production while loweringcosts
Fig. 15-10
SPE
Gulf Coast Section
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I
Fig. 15-11
SPE
International
Fig. 15-12 Halllburton
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P U T E R A S S I S T E D
E S E R V O I R M N GE M EN T
Schlumberger
Figure
15-13
Schlumberger is another major oil-field service company offering a full
range of services to the energy industry and providing virtually every type of
exploration and production service required during the life of an oil
and gas
reservoir (material included courtesy of Schlumberger).
POSC
Figure
15-14
POSC is an international not-for-profit organization whose mission is to
provide open specifications for information modeling, information manage-
ment, and data and application integration over the life cycle of E
P
assets.
FETC
Figure
15-
15
FETC is the Federal Energy Technology Center of the Department
of
Energy. Their goal is to perform, procure, and partner in technical research,
development, and demonstration to advance technology into the commercial
marketplace, thereby benefiting the environment, contributing to U.S. employ-
ment,
and
advancing the position of
U.S.
industries in the global market.
Welcome to
chlumberger
Fig. 15-13 Schlumberger
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Fig. 15-14 Petrotechnical Open Software Corporation POSC)
Fig.
15-15
Federal Energy Technology Center FETC)
17
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R E S E R V O I R H A G E M E N T
C O M P U T E R A S S S T E D
Fossil
Energy
This is the Ofice of Fossil Energy from the U.S. Department of Energy.
This web site is becoming a primary means for communicating the progress
and performance of the U.S. Department of Energy’s fossil energy program.
They manage a national technology program to increase natural gas and
petroleum supplies and provide cleaner, more efficient ways to use coal and
natural gas to generate electricity. They also oversee the Strategic Petroleum
Reserve (the nation’s emergency oil stockpile) and the Naval Petroleum and
Oil Shale Reserves.
Edinburgh Petroleum Services Figure 15-
16
EPS’ founding mission statement encapsulates the principle of provid-
ing the upstream oil and gas industry with cost effective solutions to har-
nessing the industry’s resources. EPS’ software products are based upon a
commitment to research and development. EPS software is used to leverage
skills through increasing productivity and by providing cutting edge tech-
nology that can be relied on.
1
Fig. 15-16 Edinburgh Petroleum Services EPS)
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Petroleum Technology Transfer Council Figure
15-17
The Petroleum Technology Transfer Council
(PTTC)
was formed in
1994
by
the
U.S.
oil and natural gas exploration and production
(E P)
industry to identify and transfer upstream technologies to domestic produc-
ers. PTTC s technology programs help producers reduce costs, improve
operating efficiency, increase ultimate recovery, enhance environmental
compliance, and add new oil and gas reserves.
Fig.
15-17
Petroleum Technology Transfer Council
PTTC)
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Chapter
S I X T E E N
Case
Study 1-
N ~ L Y
~ S C O V E R E D
I I E L D
EVELOPMENT
P L AN
I N T R O D U T I O N
This chapter presents an example of a
development plan for a newly discovered
field.
It
illustrates the application
of
the
reservoir management process/methodol-
ogy and the role of computer software in
making an economically viable develop-
ment plan for the field.
Offshore Wizard Field, analogous to
many Gulf Coast reservoirs, was recently
discovered by rank wildcat Well
No.
1,
and Wells 2, 3, 4, and were drilled to
delineate the field (Figs. 16-1 and 16-2).'
A
drill stem test was performed at the dis-
covery well, and the results indicated two
productive zones, 4,000 fi and 4,500
ft
sands, with
875
STBOPD and 1,456
STBOPD production, respectively. Well
No. 4
was
a dry hole, and WellNo. 3 pen-
etrated the oil-water contact.
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Gross Area,
acres 2664
Initial Pressure, psia 1930
Initial Temperahue,lF 132
Net
Thickness 1 34
Initial Oil Saturation, s
Permeability,md. 345
Porosity,
raction
0321
Oil gravity, 'WPI 37.2
Gas
Gravity
(Airl) 0.673
Bubble PointPnssurr 1616
Ioitil Gas Solubility, SCFBTBO
530
Original
Oil-In-Place, MMSTB
55.477
cture
Map
Sand
Fig
16-1
4000 fi. Sand Structure Map
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O m m
CRS
initial Pmure, psia
Initial T m p h m ,
NetThiches,
A
Initial Oil Saturation,
Pemabiiity, md
Pomity, Fraction
Oil gravity A P I
Gas
Oravity AiA)
Bubble Point Pnssur e
InitialGas Solubility,xF/STBO
Ongnal Oil-In-Place.MMSTB
Top
Structure Map
2010
2205
141
57
8
304
0 315
35 5
0 664
I 45
580
101 988
Fig. 16 2 45 ff Sand tructure
Map
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The available data indicated that this field has a significant am ount o f
po tential reserves. An integ rated team consisting of geologists, geophysicists,
petrophysicists, and structural, reservoir, drilling, completion, and equip-
ment engineers, and other professionals was charged by the management
with developing an economically viable plan for the reservoir.
D E V E L O P M E N T AND DEPLET ION S TRATEGY
Th e inpu t of all disciplines, mutual understanding, and interdisciplinary
com munication were the key to successfully developing an optim um plan.
Th e team needed to address the following main questions in order to come
up with an econom ically viable developm ent and depletion strategy:
1. Recovery scheme-natural depletion or natural dep letion
2.
Well spacing-number of wells, platforms, reserves, and economics?
augmented by fluid (water or gas injection?
Preliminary data indicated tha t the reservoir is undersaturated, having
the initial pressure several hundred psi above the bubble point. The region-
al geological data and production experience in this area suggested moderate
natural water drive
as
a potential recovery mechanism in addition to rock
and fluid expansion and solution gas drive. However, the possibility of sec-
ondary
gas
cap drive may exist, because of relatively thick pays with high
porosity and permeability.
Production from the reservoir by primary depletion and by waterflood-
ing was considered. It was also decided tha t all the wells would be com plet-
ed in the lower sands, with the plug back potential in the upper sands when
the lower sands become depleted.
It
was recognized that selective perforation
intervals in both sands would maximize the oil recovery and prevent early
high GOR production.
In order to realistically forecast oil production rates and reserves, a 111
field reservoir sim ulation study was necessary. Considering several well spac-
ings for the field developm ent, the simulated production performance results
were used to economically optim ize the num ber of wells and platforms.
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R E S E R V O I R
D T
The Wizard Field is characterized by faulted structural traps (see struc-
ture maps in Figs. 16-1and 16-2).
It
consists
of
two main oil-bearing, high-
ly porous and permeable sandstone formations. The upper
4,000 fi
sand is
separated by some
500
fi of shale from the lower 4 500 fi sand and both of
the formations are intersected by sealing faults. The formations consist of
interbedded shales and sands, however, the shales do not appear to be con-
tinuous. Based upon permeability variation, the upper and lower sands could
be subdivided into two and three layers, respectively. Pertinent reservoir data,
which were considered to be reliable, are listed below:
Figure
16-1:
Structure map and reservoir data for
4,000 fi
sand
Figure 16-2: Structure map and reservoir data for 4 500
fi
sand
Table 16-1 Data and sources
Table 16-2: Layer data
DATA
Structure and Isopach
Maps
Reservoir Pressure and
Temperature
Porosity
Permeability
Fluid Saturations
SOURCES
Seismic Surveys, Revised
WithWell Log Information
Drill Steam Test on W ell
1
Well
Logs
(Sonic, FDC-
CNL,
GR,
ILD, etc.) from
Wells 1 , 2 , 3 , 4 , and
5,
and
Conventional Cores from
Well 2
Conventional Cores from
Well
2
Well
Logs
from Wells
1 ,2 ,
Table 16-1 Wizard Field Data and Sources
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These data could be stored in a comprehensive geoscience-engineering
database for future use.
R E S E R V O I R M O D E L I N G
Considering development of the field using 40, 80, 120, and 160-acre
well spacings, a 111-field reservoir simulation model was constructed to pre-
dict depletion drive performance.A commercial black oil simulator was used
with grid cells in
30
columns and
23
rows, oriented along the faults. The
properties for both sands were obtained from data given in Table 16-2. The
reservoir layers were considered continuous and homogeneous throughout
the field. It should be pointed out that the foundation for the field develop-
ment study is the reservoir description and the reservoir engineer is heavily
reliant on this data. The vertical to horizontal permeability ratio was chosen
to be 1 to 10 based upon the available conventional core data. PVT and rel-
ative permeability data were based upon correlations, using the appropriate
parameter values.
Most wells would be initially completed in the lower sands, except for
the few wells that would encounter oil-bearing column only in the upper
Sand Net.
S.S.
ft Ft
Y
m
Reservoir Layer
Top
Thick.
Porosity
Permeability
4000 Sand
A
4351 12 32.2 298
B 4384 22 32.1 393
4500 Sand A 4822 13 31.7 221
B 4854 24 31.5 288
C 4911 20 31.4 403
Table 16-2 Wizard Field Layer Data
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~ V W L Y
D I S O V E R E D
b I E L D
D E V E L O P M E N T
P L A N
Primary
Depletion
Case Rate STBOPD Platforms No.
of
Wells Completion
Initial Prod. No. of Drilling&
4000’sd 4500’sd Pre- During Time
Prod. Prod. DaysNVell
40-AC
1000 1750 2 20
35 39
80-AC 1250 2000 1 10 20 41
120-AC 1500 2500 1 8 12 45
160-AC 1500 2500 1 8 7 45
Table
16-3
Wizard Field Drilling, Completion, and
Production Schedule
sands. The bottom two layers in the lower sand and the bottom layer in the
top sand would be perforated. The optimum production rates were derived
from a
Nodal
Analysis. Table 16-3 shows the schedule of wells to be brought
to production for the various well spacings, along with the initial production
rates for each sand. The model used the following well production limita-
tions: economic rate of 30 MSTBOPD, flowing bottom hole pressure of 600
psia, gas-oil ratio of 20,000 SCF/STB, and water cut of
95 .
P R O D U C T I O N
R T E S
A N D
RESERVE S
F O R E C A S T S
A 3Ox23x6-cell grid model was used to predict field-wide primary reser-
voir performance for 40,80, 120, and 160-acre well spacings. In addition to
the five layers, an impermeable layer was added to isolate the
two
sands. The
simulation runs were made with an active aquifer whose volume
is
10 times
the reservoir volume. The results show that the larger the spacing, the longer
the life, with less oil recovery (Table 16-4 and Fig. 16-3). The production
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Primary
Primary Development Followed
by
40- 80
120- 160- Waterilood
acre acre acre acre 80 acre
Parameters
Investment,
325 222 202 162 220
$MM
Reserves, 40.3 40.2 38.7 38.0 81.3
MMSTB
Reserves, 25.6 25.5 24.6 24.1 51.6
%OOIP
Economic L ife,Yrs 9 1 1 15 15 22
Payout, Yrs. 5.1 4.8 4.7 4.7 4.9
Disc. CashFlow 29.0 38.8 35.8 40.4 42.7
Return on Inv.
(DCFROI), Yo
Net Presentvalue 112 161 144 157 309
(NPV), $MM
Present Worth 1.63 2.31 2.15 2.49 3.64
Index PW)
Development 5.95 3.91 3.62 2.87 2.18
costs,
. DO
Table 16-4 Wizard Field Economic Evaluation
performances
are
shown in Figures 16-4 and 16-5 for the 160-acre well spac-
ing, which turned out to be the optimum spacing case for developing the
field, as discussed below.
A sensitivity analysis of the aquifer
size
on the recovery was made using
the 160-acre well spacing. Results, which are shown in Figure 16-6, indicate
significant recovery without any aquifer support. The oil recovery is 33.816
MM
STBO (21.5% OOIP) for no aquifer influx, as compared to 38.029
MM STBO (24.2% OOIP) with a
1O : l
aquifer
size.
An additional run was made using 160-acre well spacing for initiating
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NEWLY
D I S C O V E R E D
b I E L D
E V E L O P M E N T P L A N
0 2 4 6
8
10 12 14
Time, Years
Fig.
16-3
Effect
of
Well Spacing on Recovery
Flg. 164 Pressure and Cum ulative Produc tion vs. Time
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P
“
Time,
Years
Fig. 16-5 Effect of Water Cut and GOR
vs.
Time
45
4
35
10
5
Fig. 16-6 Effect of Aquifer Size on Rate and Cumulative
Production
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N E W L Y
DIS OVERED
~ I E L
E V E LO PMEN T
P L A N
primary depletion, followed by 8O-acre, 5-spot infill waterflooding afier
two
years. In this case, 12 water injection, 18 production, and 3 water source
wells would be needed. Water injection would be initiated at the 4,500 fi
sand and recompleted to the 4,000 fi sand as all production wells plugged
back to this sand. The production performance of this case is compared in
Figure 16-7 with the 160-acre depletion case and 10:
1
aquifer size. The oil
recovery from the waterflood operation was computed to be81.305 MMST-
BO
(51.7% OOIP), more than doubling the primary recovery.
F A C
LI
T I
E S P L A N N I
NG
The simulated production performance results were used to size plat-
forms, production decks, surface facilities, etc.
Also
drilling, well comple-
tions, and production practices requirements were established. Subsequently,
estimates of capital requirements and operating expenses were made for eco-
nomic analyses.
1 I
____.__.. .
- - -
.
.
..-
e l - ,
.a
. Waterflood
Depletion
I I I
I
0
5
10
15
20 25
Time, Years
Fig.
16-7
Cumulative Oil Production vs. Time
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C O M P U T E R A S S
S T E D
I
R E S E R V O I R h N N A G E M E N T
E C O N O M I C O P T I M I Z T I O N
Using estimated production, capital, operating expenses, and other
financial data (Table 16-4), economic analyses of the primary development
plans with 40,
80,
120, and 160-acre well spacings were made. Table 16-4
shows the evaluation results for these cases. The 160-acre well spacing case,
with the lowest capital investment, development cost, and payout time and
the highest PWI, DCFROI, and next highest NPV, offered the economical-
ly
optimum primary development plan. Even though the 80-acre case yield-
ed the highest NPV ( 161 million), the additional capital investment of 60
million over the 160-acre case ( 222 million) gave an incremental NPV
return of only 4 million.
The 160-acre development case without any aquifer support showed
project life of
13
years, DCFROI of 39%, NPV of 140 million, and devel-
opment costs per barrel of 3.12. Therefore, the 160-acre primary develop-
ment still looked economically very attractive.
Results of the economic analysis of the waterflood case (Table 16-4)
show the highest oil reserves, DCFROI,
NPV,
PWI, and lowest development
costs per barrel of oil. Therefore, the early waterflood offers the most eco-
nomic means to exploit this field. The platform needs to be designed
so
the
water injection facilities could be installed later, i.e. some deck space would
be left for future water injection equipment. Based on the economic evalua-
tion results, the team recommended to its management the initial 160-acre
primary development followed by 8O-acre, 5-spot infill waterflooding after
two
years.
I M P L E M E N T T I O N
After management approval of the project, the next major assignment
would be to implement the development plan in order to get the production
on stream
as
soon
as
possible.
A
project manager with full authority would
be needed to manage the various activities including installation of platform
and surface facilities, drilling and completion programs, acquiring and ana-
lyzing necessary logging, coring, and well test data to better define reservoir
characterization. The reservoir database needs to be upgraded, and simula-
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N E W L Y D I S C O V E R E D
F I E L
DEVE LOPMENT P L A N
tion runs have to be made with the latest data for upgrading the depletion
strategy and predicting reservoir performance.
MONITORING
SURVE I L L ANCE
A N D EVALUAT ION
An
integrated and comprehensive monitoring and surveillance program
needs to be initiated at the start of production from the field. Dedicated and
coordinated efforts of the various functional groups working on the project
are essential. The production performance of the reservoir needs to be mon-
itored and reviewed periodically to ensure the development plan is working.
This can be done effectively by comparing the actual well/reservoir produc-
tion and pressure behavior with the simulated performance.
R E F E R E N C E S
1.
Satter,A., Varnon, J. E., and Hoang, M. T.: “Reservoir Management:
Technical Perspective,”SPE Paper 22350,
SPE
International Meeting
on Petroleum Engineering, Beijing, China, March 24-27, 1992
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S E V E N T E E N
Case
Study 2
I N T R O D U T I O N
The mature North Apoi/Funiwa
Field (Fig. 17-1), operated by Texaco
Overseas (Nigeria) Petroleum Company
Unlimited (TOPCON), is located
off-
shore Nigeria (Fig. 17-2). North Apoi was
discovered in 1973, followed by Funiwa,
an extension of North Apoi, in 1978.
The fields consist of north-wesdsouth-
east tending anticline with several major
faults (Figs.
17-3
and 17-4), and multiple
sand reservoirs at depths between 4,900
fi
ss) and 7,000
fi
ss) (Fig. 17-5). Figure
17-6 shows typical log and core analysis
data of North Apoi Well 34. Basic reser-
voir data for Ewinti and Ala Series are
shown in Table 17-1. Sixty-four wells,
including
58
commercial wells, were
drilled
as
of June
30,
1995. Primary pro-
ducing mechanisms are a combination of
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Fig.
17-1
North ApoWuniwa Life Cycle
Fig.
17-2
North ApoWuniwa Field Location Map
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M T U R E FIELD
STUDY ^
N O R T H A P O I ~ F U M I W A
Fig. 17-3
North ApoWuniwa
Field
depletion, gas cap, and natural water drives. Ewinti-5, -6, -7 and Ala-3, -5,
-7
are the major producing sands.’
An integrated team of geoscientists and engineers from TOPCON,
Nigerian government organizations, and Texaco
E P
Technology
Department (EPTD) were charged to review the field’s six largest reservoirs
in three phases to evaluate and capture the upside potential of the reserves.
The first phase study involved the Ewinti-5 and Ala-3 reservoirs, which
contain more than 50 of the field’s booked reserves (Fig. 17-7).This work
was initiated in March and completed in June, 1995 at EPTD facilities in
Houston. The second phase study, covering the Ewinti-7 and Ala-5 reser-
voirs, and the third phase study, covering Ewinti-6 and Ala-7 were
also
car-
ried out in Houston, taking three months each.
These integrated studies exemplify the close alignment between EPTD
and TOPCON in the transfer and application of leading edge technologies
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E S E R V O J
R
M A N A G E M E N T
P U T E R A S S S
E D
WellF-10
6oo
500 700
1300
1400
1500
1600
1700
1800
1900
2000
2100
2000
Fig.
17-4
North ApoWuniwa Field Seismic Line
Fig. 17-5 North ApoVFuniwa Stratigraphic Cross Section
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MATURE FIELD S T U D k ’ : T
N O R T
A P O I I F U N I W A
in support of TOPCON’s growth plan. The results of these mature-field
studies are presented here.
O B J E C T I V E S
The objectives of the studies were to determine:
ultimate primary recovery
optimum recovery with additional vertical and horizontal wells,
EOR
potential
and workovers, including gas lift
C H A L L E N G E S
Challenges faced in the studies were:
mature reservoirs
declining production
increasing operating cost
unrealistic recovery factors
need to enhance asset value
Fig. 17-6 North Apoi Well 34 Log and Core Data
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EWINTI-5
DEPTH - FT SS
5000
TRAP STRUCTURE/
ROCK TYPE UNCONSOLIDATED
FAULTING
SAND
GROSS THICKNESS - FT 70-1 30
POROSITY - 30
PERMEABILITY - MD 1500
INITIAL PRESSURE
-
PSlG
2200
RESERVOIR
TEMPERATURE - F 165
INIT. OIL FORM. VOL. FACTOR
1.2
OIL VlSCOSlTY - CP 1.5
OIL GRAVITY
-
API
28
GAS GRAVITY (AIR
=
1) 0.6
DRIVE MECHANISM GAS CAP/
STRONG
WATER DRIVE
85
INITIAL SOLN. GOR
-
SCF/STB
364
ORIG. OIL IN PLACE
-
MMSTB
293
CUM. PROD. 12/94 - MMSTB
Table 17-1 North ApoilFuniwa Field General Data
ALA-3
7000
STRUCTURE/
FAULTING
UNCONSOLIDATED
SAND
50-1 70
20-25
500-1500
3000
222
940
1.6
0 5
40
0.7
GAS CAP/
WEAK
WATER DRIVE
214
47
EWlNTl-6
5
EWINTIS
31
EWINTI-7
16
ALA-3
21
ALA-7
5
Other
9
Fig. 17-7 Ewinti and ALA
Reserves (as of Dec. 31, 1994)
DATA
ORGANIZATIONS PROFESSIONALS
INTEGRATION/
ALLIANCE
TECHNOLOGY TOOLS
Flg, 17-8 IntegratiodAlliance
2 0 0
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M T U R E
FIELD STUDY:
N O R T H A P O I I F U N I W A
A P P R O A C H
The approach taken was:
1. Review geoscience and engineering data
2
Classical material balance analysis
3.
Decline curve analysis
4. Reservoir simulation analysis
a. reservoir description
b. full-field performance history match
c. full-field performance prediction
5.
Plan strategies and forecast performance
a. under existing conditions
b. with workovers and infill wells
c. with gas lift
d. with water injection
TECHNOLOGY
OPERATIONS
CORPORATE
ALLIANCES
GOVERNMENT
Fig. 17-9 Organizational Alliance
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U T E R A S S
I S T E D
E S
E R
V O
R f l A N A G
EMEN
T
D E L I V E R A B L E S
At the outset of the studies,
all
parties agreed on the following deliverables:
Improved reservoir description
Updated
OOIP
Reserves addition
Better reservoir management skills and strategies
Technology transfer/application
The studies utilized integration/alliance (Fig. 17-8) of organizations
(Fig. 17-9), data and software (Fig. 17-10), professionals working together
as
a team (Fig.
17-11
and Table 17-2), tools and technologies (Fig. 17-12).
Multidisciplinary data used and their sources are listed in Table 17-3.
SEISM'C
GEOLOGICAL
LOGGING
CORING
DATA
FLUID
PRODUCTION
WEL L TEST
Pc
UNlX
WORKSTATION
WORKBENCH
-RESERVOIR
DESCRIPTION
-PRODUCTION
DATA ANAL YSIS
-WELL TEST
ANALYSIS
(SIMBEST 11
GR DSTAT
OILWAT
NAPS
3D
VISUALIZE
-SIMULATION
Fig. 17-10 Data and Software Integration
2 0 2
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M T U R E FIELD STUDY ^
N O R T H A P O I I F U N I W A
Integrated geoscience and engineering models were developed using
revised maps based upon reprocessed and re-interpreted 3-D seismic survey
data of
1986.
Well log and core analysis data, rock and fluid properties, well
test data and other engineering data, plus
20 years of field production histo-
r y
were also incorporated into the reservoir description.
DECLINE
C U R V E
ANALYSIS
Integrated Petroleum WorkBench software
of
Scientific Sohare
Intercomp (SSI) consists of reservoir description, well test analysis, produc-
tion data analysis, and black oil simulation modules. The production data
analysis module was used to make decline curve analyses on
all
wells where
it was appropriate, i.e. wells that had established declining production dur-
ing a period where well conditions had not changed appreciably. The results
were compared to the simulation predictions.
GEOLOGIST GEOSTATISTICIAN
GEOPHYSICIST ENGINEERS
PROFESSIONALS
PETROPHYSICIST COMPUTER
SCIENTIST
51 MULATlON SPECIALIST
Fig. 17-11 Integration of Professionals
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Team
Texaco Nigerian Division
Engineers
3
Ccophyrldst
eologlst 1
Nigerian Government
Professionals
*
Engineers 2
Texaco
EPTD
Specialists
h p h g s i e a -1 ProJeCr
Computer support
I
MPnngement-1
Englneerlng
3D Vlsunllufion
* CwJtntlltica
1 Reservolr
Horizontal
Wells-
2
Petrophyrica
-1
Texaco Management Support
and Commitment
Table
17 2
Team Members
Data
Sources
DATA
SOURCE
STRUCTURE ISOPACH
MAPS
D
SEISMIC WELL LOGS
W R O S l ~ .
WFLL LOGS CORES.
PERMEABILITY. FLUID CORRELATIONS
S A W RATIONS
FLUID CONTACTS WELLLOGS
FORMATION OPS
RESERVOIR PRESSUR E WELL TESTS
TEMPERATURE
PVT PROEXTIES
BOITOM HOLE S MPLES
CORRRATIONS
RELATIVE CORES CORRELATIONS
PERMEABlLlTlE
S
PRODUCTIONRATES
H W R Y SUMMARY
WELL T ST ALL4XATION
Table
17 3
Data
Sources
204
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M A T U R E F IE L D S T U L W : ~
N O R T
A P O I I F U N I W A
C L A S S I C A L M A T E R I A L
B A L A N C E
Classical material balance analysis was considered to be a pre-requisite to
reservoir simulation. The EPTD-developed OILWAT material balance sobare
was used for estimating original oil-in-place and primary drive mechanisms.
The material balance analysis showed that the primary production mechanism
of the Ewinti-5, Ewinti-7 and Ala-5 sands is strongwater drive, with addition-
al support from gas cap driveand solution gas drive. The Ala-3 reservoir demon-
strates weak water drive plus
gas
cap drive and solution
gas
drive.
Original Oil in Place
(OOIP)
calculations provided comparisons with
the simulation results (Fig. 17-13), while the drive mechanisms were used
as
input to the simulation model descriptions.
RESERVOIR SIMULATION
SSI’s black oil simulator was utilized for full-field performance history
match and forecasts. The stepwise history matching procedure consisted of
3D SEISMIC
PETROPHYSICS
WELL LOG
ANALYSIS
DRILLING RESERVOIR
COMPLETIONS ENGINEERING
TECHNOLOGY
RESERVOIR
SIMULATION
PRODUCTION
ENGINEERING
GEOSTATISTICS
3D VISUAL IZATION
Fig. 77 12 Integrationof Technology
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pressure matching followed by saturation matching. Pressure matching was
achieved by specifying the historical total three-phase voidages for the wells,
while adjusting pore volumes, aquifer strength, and fault connections.
Three-dimensional seismic survey data were re-examined for validating the
reservoir models. Pressure matching ensured that the reservoirs historical
total (three-phase) voidages were duplicated both for the total reservoir and
for each of the wells.
The
OOIP
values estimated from classical material balance analyses and
simulation techniques are comparable to each other but are substantially
higher than the previously booked values (Fig. 17-13).
Good history matches using the black oil simulator were achieved for
most of the wells by adjusting the usual reservoir parameters within their
accepted ranges of uncertainty. Difficulties matching a few wells, however,
led to questions about the structure maps. Here, the interaction between the
geoscience and engineering members of the team proved very beneficial. The
3-D
seismic survey data were re-examined to validate the reservoir model.
Ultimately, some areas of poor seismic resolution were re-interpreted, lead-
ing
to
successful history matches in all wells.
After reservoir performance history matching using the black oil simu-
lator, model prediction runs were made under various investment scenarios
for optimally draining the reservoirs, including additional take points, hori-
zontal wells, gas lift, and water injection. Opportunities were identified for
performing workovers and placing additional wells to improve drainage in
the Funiwa area.
Nodal analysis sohare (NAPS) was used to treat wellbore hydraulics.
Computed WorkBench results were imported into a 3-D visualizer
(REVIEW) for more comprehensive viewing of the results. Performance
forecasts for the remaining period of the production contract were made
under different operating scenarios in order to determine the optimum
development plan
as
follows:
Case 1: Primary depletion with the current wells and production
limitations (Base Case)
Case 2: Base Case Infill Wells Workovers
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I
Fjg.
17-13
Original Oil in Place
Case
3:
Case 2
Gas
Lift
Case 4: Case
3
+ Water Injection, applicable to Ala-3 sand
Since the Ala-3 reservoir has weak natural water drive, the studies
showed that recovery could be improved with water injection. The Ewinti-
5,
Ewinti-7, and Ala-5 reservoirs, on the other hand, have strong water
drives, and thus no water injection case was attempted.
The estimated reserve increases from the simulation studies are given in
Figure
17-14.
The new estimated reserves are substantially more than the
previously booked values. Recommendations for the six reservoirs studied in
Phases 1, 2, and
3
include 10 horizontal wells, 4 deviated wells,
1
replace-
ment well, and workovers.
All
infill and workover wells are located in the
Funiwa field. Since the current drainage patterns in the North Apoi area are
adequate, no additional offtake points are necessary there.
207
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The placement
of
the wells was determined from the simulator-calcu-
lated fluid saturation distributions initially and throughout the producing
life of the reservoirs. Figure 17-15, for example, shows the oil saturation dis-
tributions in the Ewinti-5 Layer 4 model initially and at the time of the
study 1995), plus the predicted distributions at the end of the lease expira-
tion (2008), for the base case and for the infilUworkover cases. The locations
of the horizontal wells shown in this figure were based upon high remaining
base case saturation predicted for 2008.
TOPCON and the Nigerian government acted quickly on the study rec-
ommendations. Within nine months from the start
of
the Phase
1
study,
two
successful horizontal wells were drilled and completed in the Funiwa Ewinti-
5 reservoir (Table 17-4).The first well came on production at 2,670 BOPD
of oil from a 700-foot horizontal section. The second well has a 1,600 foot
horizontal pay section and produced at 4,020 BOPD.
Fig.
17 14 Reserves Addi t ion Summary
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M A T U R E F I ELD
S TUDY:
)
N O R T H
P O I l
F U N I W A
Fig.
17-15
Oil Saturation Distribution
C O N T R I B U T I O N S
The role played by each partner in this alliance-operations, tech-
nology, government, and vendor-was essential to the successful outcome
of the project.
TOPCON, the operator, recognized both the need for the investigation
to be made and the benefits of collaboration. Their engineers and geoscien-
tists provided all the field data plus an in-depth knowledge of reservoirs and
current producing operations. They performed a majority of technical proj-
ect work themselves.
EPTD, the technology center, provided project coordination, computer
software and hardware, software training, and specialized expertise in 3-D
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WELL WELL
F-32 F-33
ZONE EW-5 EW-5
1600 700
ORIZONTAL
SECTION
(ft)
BOPD
4020 2670
BS W (%)
0
GOR 237 260
Table
17-4
Funiwa Ewinti 5 Reservoir Horizontal Well Results
seismic interpretation, well log analysis, reservoir simulation,
3-D
visualiza-
tion, and horizontal drilling.
Nigerian government engineers took an active role in project work.
Their participation ensured that
all
regulations would be met and the gov-
ernment’s interests were considered early in the planning of proposed oper-
ations. This led to rapid approval and early commencement of drilling.
SSI, the WorkBench software vendor, provided consultants who con-
tributed significantly to the timely completion of the projects. They
arranged for the availability of extra s o h a r e licenses for the project and pro-
vided technical support for this first major project at EPTD using their
WorkBench product.
The joint efforts resulted in significant cycle time reduction and
set an
excellent example of integration and alliance.
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C O N C L U S I O N S
T he conclusions made from the integrated study were:
3-D seismic improved reservoir description
O O IP was revised by m ore than
50
Upside potential
of
reserves additions is significant
Recovery factors estimated from 3 0% to 55%
Teamwork and integration were critical to project success
This
study sets an example for effective technology transfer and application
T O P C O N has since completed several other
studies
based upon the success
of
this
one. The reserve additions resulting &om these studies are substantial.
R E F E R E N C E S
1.
Akinlawon, Y., Nwosu, T., Satter,
A.
and Jespersen,
R:
“Integrated
Reservoir Management Doubles Nigerian Field Reserves,” Hart’s
Petroleum Engineer International, O ctober 1996
211
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Chapter
I G H T N
Case
Study
3-
W
TER
F
L
19
D
P R O
J
EC
T
E V E L0
M E N
T
I N T R O D U C T I O N
A field that was discovered many
years ago is now depleted. It consists of a
simple domal structure, and five
of
the
nine wells drilled were producers (Fig. 18-
1). Primary producing mechanisms were
fluid and rock expansion (reservoir pres-
sure above the bubble point), solution
gas
drive, and limited natural water drive.
There is limited data availability. Even the
gas, oil, and water production data are
somewhat unreliable. Reservoir pressures
were not routinely monitored. This is typ-
ical when dealing with old, mature fields.
A n
integrated team of geoscientists
and engineers was charged by the man-
agement to review past performance and
investigate waterflood potential of this
field (hypothetical, but akin to real life
reservoirs). The team’s approach was to:
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Fig.
18-1
Top Structure
Map
build an integrated geoscience and engineering model of the
reservoir using available data and correlations for fluid PVT and
relative permeability
carry out a conceptual simulation of full-field primary performance
without history matching since no historical pressure data or
reliable production volumes could be obtained
forecast performance under peripheral and pattern waterflood
This chapter presents the results of the study. Although the field is not
real, much can be learned about how to engineer a waterflood project, even
with incomplete data.
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IWATERFLO D PROJECT
E V E L
O P M N T
R E S E R V O I R
D A T A
An analysis of the logs from the nine wells showed that the reservoir het-
erogeneity could be represented by five producing horizons. Permeability
was computed from a correlation of porosity vs. permeability. Permeability
in the x and y directions are considered to be the same, i.e., no directional
permeability. The vertical to horizontal permeability ratio is assumed to be
0.1. Layer properties are presented in the figures as listed below:
Tables 18-1 through 18-5: Structure tops, gross and net thicknesses,
porosities, and permeabilities for Layers
1-5
Figures 18-2 through 18-5: Gross and net thickness, porosity, and per-
meability of Layer 1 as an example
Figure 18-6: Cross-section
Fig.
18 2
Gross Thickness-Layer
1
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CO P U T E R A S S S T E D
f E S E R V O I R h N A G E M E N T
Well No.
1
2
3
4
5
6
7
8
9
HTOP
-4216
4239
-4233
-4230
-4260
-4274
-4285
-4280
-4277
GROSS (ft)
12.0
10.5
9.5
11.0
9.0
7.5
6.5
7.0
8.0
NET (ft)
12.0
10.5
9.5
11.0
9.0
7.5
6.5
7.0
8.0
PHI
( )
24.0
21
o
19.0
22.0
18.0
15.0
13.0
14.0
16.0
Kx (md)
759
178
68
288
42
10
4
6
16
Table 18-7 Reservoir Descrip tion: Layer No.
1
Well No. HTOP GROSS
( f t )
NET
(ft)
PHI ( )
Kx
(md)
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
16.0
14.0
12.5
14.5
12.0
10.0
8.5
9.5
10.5
16.0 23.0
14.0 20.0
12.5 18.0
14.5 21
o
12.0 14.0
10.0 14.0
8.5 12.0
9.5 13.0
10.5 15.0
Table
78-2
Reservoir Descrip tion: Layer
No. 2
Well No.
1
2
3
4
5
6
7
8
9
HTOP
n/a
n/a
rda
rda
nla
n/a
n/a
n/a
n/a
GROSS
(ft)
21
o
18.5
16.5
19.5
15.5
13.0
11.5
12.5
14.0
NET
(ft)
21
o
18.5
16.5
19.5
15.5
13.0
11.5
12.5
14.0
PHI ( )
22.0
19.0
17.0
20.0
16.0
14.0
12.0
13.0
15.0
68
110
42
178
26
6
2
4
10
Kx md)
288
68
26
110
16
6
2
4
10
Table 78-3 Reservoir Descriptio n: Layer No.
3
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The reservoir contains
33”
API crude oil at 2 ,332 psia original reservoir
pressure and 123°F temperature. It is undersatura ted, having the initial pres-
sure several hundred psi above the bubble point 1,855 psia). Fluid proper-
ties and gas-oil and water-oil relative permeability data that were obtained
from correlations, are shown in Figures 18-7 through 18-10.
RESERVOIR MODELING
Using Baker Hughes SSI’s black oil simulator with a 2 5 x 2 5 ~ 5rid 3,125
cells, each 1O2 acres), a full-field reservoir simulation model was constructed
to predict primary performance. The model used the following well produc-
tion limitations:
Well
No.
1
2
3
4
5
6
7
8
9
HTOP
nla
nla
nla
nla
n/a
nla
nla
nla
nla
GROSS (ft)
11.0
9.5
8.5
10.0
8.5
7.0
6.0
6.5
7.5
NET ft)
11.0
9.5
8.5
10.0
8.5
7.0
6.0
6.5
7.5
PHI
( )
21 o
18.0
17.0
19.0
16.0
13.0
11.0
12.0
14.0
Table 18-4 Reservo ir Desc rip tion: Layer
No. 4
Well
No.
1
2
3
4
5
6
7
8
9
HTOP
nla
nla
nla
nla
nla
nla
nla
n/a
nla
GROSS
tt)
13.0
11.5
10.5
12.0
10.0
8.0
7.0
7.5
8.5
NET (ft)
13.0
11.5
10.5
12.0
10.0
8.0
7.0
7.5
0.5
PHI
( )
20.0
17.0
16.0
18.0
15.0
12.0
11.0
12.0
13.0
Kx
md)
178
42
26
68
16
4
1
2
6
i
md)
110
26
16
42
10
2
1
2
4
Table 18-5 Reservo ir Descrip tion: Layer No. 5
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E S E R V O I R h N A G E M E N T
P U T E R A S S S T E D
Fig.
18-3
Met Thickness-Layer 1
Fig.
18 4
Porosity--Layer 1
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(- A T E R F L O
B E V E L O P M E N T
P R O J E C T 7
Economic oil production rate, STB/day
Maximum
gas-oil
ratio, SCF/STBO
Maximum water cut,
Yo
Minimum bottom hole pressure, psia
Completed layers
1A 3
The original oil-, gas-, and water-in-place were computed to be
26.2
MMSTB, 10.8 BSCF, and 19.1 MMSTB. The reservoir pore volume was
50.1
STB. Primary oil recovery was
4.1
MMSTB
(15.7
OOIP) after
8.3
years. Cumulative
oil
productions from the individual wells were:
10
2,500
95
150
Well Cumulative Oil Status
Production
1 1,418.0
MSTBO Producing
2 604.9
MSTBO Shut-in due to highGOR
3 687.1 MSTBO Shut-in due to high GOR
4 930.9
MSTBO Producing
5
467.2
MSTBO Shut-in due to high
GOR
Fig.
18 5
Permeability-Layer
1
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Fig.
8 0 0 -
P
6oo
g
g
8400
-
a2
(3
-
0-
Fig.
2 2 0
4 -
-
g 3
f
2 -
-
1-
18 6 Cross Section
ooor
5r 4.1:~
I
I
1
I I
3.1-
Bubble
m - Point
c
t
2.1-
2 :
3
:
.1
-
s
p
2
0.1-
0.90
sw
ldoo
15bo
2doo
2630 3000
Pressure,
PSlA
18 7
Oil Properties
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Figures 18-1
1
and 18-12 show field-wide production performance.
P R O D U C T I O N R A T E S A N D
R E S E R V E S
Performance forecasts for waterflood recovery were made for the follow-
ing operating scenarios in order to determine the optimum development
plan (Fig. 18-13):
Case 1
-
Peripheral waterflood, using the existing five producing
Case
2 -
Enhanced peripheral waterflood, with eight injectors and
Case
3
- Single 5-spot pattern waterflood, using the central existing
wells and four dry holes at the periphery as injectors
nine producers
producing well and converting four surrounding wells to injectors
Pressure, PSlA
Fig.
18 8 Gas
Properties
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Fig.
Liquid Saturation, FRPCTlON
18 9
Gas Oil Relative Permeability
1.0 I
I I I l
in
.0.8-
2
I
d
g -
1 :
P
B
y
0.6
-
c .
i
I
0 2
-
0
0.2
0.4
0.6
0.8 1.0
Water SaturaUon,FRACTION
Fig. i8 10 Water Oil Relative Permeability
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Case
4
-
Four 5-spot pattern waterfloods, using nine injectors
and
Case
5
- Multiple rows of direct line drive waterfloods, using 13
four producers
injectors and 12 producers
Calculated results for these cases are presented
as
listed below:
Table 18-6: Compares 10 years of annual waterflood oil productions
Figure 18-14: Compares 10 years of primary and 20 years
of
Figure 18-15: Compares waterflood oil recovery vs. pore volume
waterflood oil productions
of
water injected
Project life being the same, Table 18-6 and Figure 18-14 show signifi-
cantly higher recoveries in Cases 2
and
5, which have more injectors and pro-
ducers than the other cases. Case
3,
with only one producer shows the least
recovery. Or in other words, recoveries are directly related to the number of
TIME
Fig. 18 11 Fieldwide Primary Production Rates and
Pressure Behavior
2 2 3
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E S E R V O I R
FlekMrb
01,
WnW,
and
Gas
C u m
-
3
I
0
M w
Flg. 18 72 Fieldwide Primary Cum ulative Oil, Water, and Gas Productions
Case
1
Case
2
Case 3
Case1 Peripheral 5 4
Case2 Peripheral 9 8
Case3 Pattern
1
4
Case4 Pattern
4 9
Case5
Pattern 12 13
Case
4
Case 5
Fig.
18 13
Developm ent Cases
224
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Year
1
2
3
4
5
6
7
8
9
10
Total
C a s e 1 C a s e 2
69.8 275.2
49.8 189.7
48.7 451.5
51.6 656.3
55.0 605.7
60.8 515.6
134.8 460.6
234.7 407.6
242.1 352.0
241.4 305.1
996.0 4,219.5
Case
3
37.5
27.1
27.5
28.3
30.0
35.7
54.0
93.5
155.5
221.1
710.2
C a s e 4 C a s e 5
82.0 279.8
127.4 786.2
321
O
882.4
325.8 605.9
293.1 486.8
266.5 405.6
248.8 341 o
237.0 288.9
228.1 253.7
221.3 220.3
2,351.3 4,550.6
Table 18 6 Produced Oil Volumes
production wells. Figure
18-15
shows that Case
5
is the least efficient recov-
ery scheme when injected water requirements are considered.
More oil recovery with more injection and production wells may not
provide the best economically viable scheme. That can only be determined
by economic analysis
of
the cases considered.
Also,
the limited cases consid-
ered here do not necessarily give the answer to best develop the field for
waterflooding. These examples simply demonstrate that more than one way
of developing the field needs to be investigated in order to determine the
potentially most economically viable project.
ECONOMIC EVALUATION
Making a sound business decision requires that the project will be eco-
nomically viable. That is, it will generate profits satisfying the economic
yardsticks of the company. An outline of the procedure for economic evalu-
ation is given in Figure
18-16.’
Economic optimization is the ultimate goal of sound reservoir manage-
ment. It involves more than one scenario or alternative approach to picking
the best solution.
For
example, possible choices and questions concerning
2 2 5
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12000
* 8000
E 6000
f
-a
g
4000
a
d
u
2000
0
0.00 5.00 10.00
15 00
20.00 25.00 30.00
T i me (Yea n )
. + Depletion
ase-1
-
Case-2
+Case-3 +Case4 +Case5
Fig. 18 14 Cum ulative Oil Recovery vs. Time
0.5
0
0
0.25
0.5
0.75
PV Water Injected (fraction)
-a-Case-1 -@Ca2 + C a seJ
+Case4
+Case-5
1
Fig. 18 15 Oil Recovery vs. Water Injected
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the recovery scheme and development plan for a waterflood project:
1. Peripheral vs. inside pattern flood
2. Well spacing- number of wells
The economic analyses and comparisons of the results of the various
choices can provide the answer to making the best business decision to max-
imize profits.
Yardsticks for measuring the value of investments and financial oppor-
tunities include:
Payout Time he time needed to recover the investment. i.e., the
time when the undiscounted or discounted cash flow CF
=
revenue - capital investment
-
operating expenses) is equal to zero
Pro@
to
Investment
R a t i ~
he total undiscounted cash flow without
capital investment, divided by the total investment
Present Worth
Net
Prof
PWP):
he present value of the entire
cash flow discounted at a specified discount rate
Investment Eficiency or Present Worth I n h x or ProjhbiLity Index
PAYOUT
DCFROI Objective Scenario
PWNP
Production
Investments
Operating Expenses
OiVGasPrice ~
I I
Make
Risk
Optimum
Fig.
18 16 Econom ic Optimizat ion
2 2 7
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Data
Oil and gas production
rates vs. time
Oil and gas prices
Capital investment tangible
intangible) and operating
costs
Royaltylproduction sharing
Discount and inflation rates
State and local taxes production,
severance, ad valorem, etc.)
Federal income taxes, depletion
and amortization schedules
Source/Comment
Reservoir engineers1
Unique to each project
Finance and economic
professionals1
Strategic planning
interpretation
Facilities, operations and
engineeringprofessionals1
Unique to each project
Unique to each project
Finance and economics
professi onalsl
Strategic planning
interpretation
Accountants
Accountants
Table
18 7
Economic
Data
(PI):The total discounted cash flow divided by the total
discounted investment
Rate of Return:The maximum discount rate that needs
to
be
charged for the investment capital to produce a break-even venture.
ie. ,
the discount rate at which the present worth net profit is equal
to zero
Discoclnted
ab
Flow Return o Investment DCFROI)
or
Internal
The
data required for economic analysis can be generally classified as
production, injection, investment and operating costs, financial, and eco-
nomic data. Table
18-7
provides a list of pertinent data.
2 2 8
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WATERFLO
D
PROJECT
B E V E L O P M E N T
Th e five waterflood design cases were analyzed to determ ine the poten-
tially most economically viable project. Th e procedure used for economic
calculation before federal incom e tax BFIT) is outlined below:
1.
Calculate annual revenues using oil and gas sales from production
2.
Calculate year-by-year total costs including capital investments
and un it sales prices
drilling, com pletion, facilities, and abandonment, etc.), operating
expenses and production taxes
3. Calculate annual undiscounted cash flow by subtracting total costs
from the total revenues
4. Calculate annual discounted cash flow by multiplying the undis
counted cash flow by the discount factor
at
a specified discount rate
The computational procedure is illustrated in
a
spreadsheet calculation
for Case
2
in Table
18-8.
The results of the econom ic analysis for the five waterflood cases are pre-
sented in Table 18-9, which shows that all the cases are very favorable for
waterflooding. Note that federal income taxes are not taken in to account.
Case 3 gives the lowest amount of investment, reserves, and develop-
ment costs, yet has very favorable discounted cash flow return on investment
and the highest
profit-to-investment-ratio.
Case
2
for peripheral flood and
Case 5 for pattern flood show the most promise. Case 5 shows the highest
present worth net profit; however, this requires 80% more capital than for
Case
2,
which gives about th e same present worth net profit and better dis-
counted cash flow return on investment.
It
should be realized that for the Case
2
peripheral flood , recovery esti-
mates m ight be optimistic, because the reservoir layers were considered to be
homogeneous and continuous. This may not represent the real situation.
O n the other hand, Case 5 for the pattern flood is better suited to treat reser-
voir heterogeneity and reservoir discontinuity.
The selection of the op timum case will depend on availability of capital,
technical considerations, and the risk involved. Th e sensitivity analysis dis-
cussed below shows tha t Case
2,
even if recovery is 20 lower, may still be
the best choice.
2 2 9
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N
w
iD
g
1897
I
1998
275.21 I
1898
189.7
I 2w3
515.83
I
x x 46057
2MIs 407.66
2wB
351.97
2007
3M06
2ms 235.47
2009 227.9
2010
227.69
2011 227.9
Tola1
5138.47
9)
logs
2 5
m
2 7
2 8
2010
2011
OilRice
ww
1950
19.50
19.50
19.50
19.50
19.50
19.60
19.50
19.50
19.50
1950
1950
19.50
19.50
1950
(10)
Dieeaml
FasMr
021
0.-
0.8437
0.7555
0.6726
0-
0.5362
0.4707
0.4274
0.3816
0 3107
0.3042
0.27l6
0.2425
02166
0.1w
01 -YB
W
1)Wim
0.00
5.37
8.81
12.80
11.81
10.05
8.98
7.95
6.88
5.95
4.58
4.44
4.44
4.44
1002M
3 m
11)
CiSCW led
1hIEEi d
Marl)
(9) (10)
4.420
0.m
0 W
0.m
0.m
0.m
O Oo0
0.W
0.m
0 . m
0.m
0.m
0 . m
0.m
0.W
4.460
Gas
Rad.
W S R
1610.5
4.12
37.85
2889
28.89
28 89
2706.87
20 77
(12)
0pw.Il g
OSI
W)
0.318
0.318
0.318
0.318
0.318
0.318
0.318
0.318
0.318
0.318
0.318
4.452
asm
QnnscF)
2.10
2.10
2.10
210
2.10
2.10
2.10
2.10
2.10
210
2.10
2.10
2.10
2.10
2.10
(13)
TOW ost
am)
8k WW
4.678
2.m
1289
2.116
2.909
2.710
2.854
2.137
1.928
1
m
1
524
1.249
1.219
1219
1.423
90511
insRevrrue
urn)
4)x 5)11m
0.m
3 392
1.m
0.167
0.156
0.147
0.127
0.114
o rm
0.W
0.079
0.m
0.061
0.061
0.061
5.664
14)
U n d ) a m W
Wuh Flow
p3w
rn(13)
4.878
6.651
3
8.876
1o.w
9248
7.827
6.958
6.128
5.245
4.504
3 . a
3.286
3.286
3.082
75,374
r a-
sm)
a+@)
0 W
8.749
4.766
8.992
12.053
11.858
10.181
0.095
8.052
6.053
6 W
4 . 6 s
4.505
4.505
4.505
105.886
15)
DImnted
Cash Mu
012
Ww
lOH14)
4.m
5.626
2.828
4.624
6.082
4.858
3.747
2.974
2.237
1.787
1 3m
0.925
0.787
0.711
0.596
34 702
PrcduchpT&x
( s w
7)xmFmln
0.m
1.750
0.851
1.708
2591
2.392
2.038
1.819
1.610
1.391
1206
0.Bl
0.801
0.w1
21.177
o.om
(16)
M U l a t l V e
Discounted
c sh
Flow
012
W
4.m
1.216
3.842
8.467
14.498
19.457
232 4
28.178
28515
30.w
31.673
32598
na9
34.106
34 m
Econom
o
2
3
4
5
6
7
8
9
10
11
12
13
14
15
old 4.w
Payout Time(years)=l.ig
Profit-to-investmentRalio=16.44
Present
Worth Net
Prolit ($MM)=
34.702
D i m n t e d
Cash
Flow
Return on Invegbnent( )= 131.15
PresentWorth index=7.781
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WATERFLO D PROJECT
(-
E
E L O P M E N T
Capital Investment,
SMM
ReserVeS,
MMSTBO
Project
Life
Payout,Years
DisccuutedCash
Flow
Rehun
on
Investment,
PresentWorthNet
Profit,
MM
profit-t*lnvestment
Ratio
PresentWorth
Index
DevelopmentCosts,
VSTBO
ECONOM IC EVALUATION RESULTS
Case4
1.853
1.965
I5
2.58
69.64
9.454
16.88
5.66
0.94
Case-2
4.882
5.138
15
1.78
131.15
34.702
16.44
7.78
0.95
Case-3
0.973
1.378
15
2.44
80.12
7.013
23.32
8.02
0.71
Case-4
3.484
3.176
15
2.74
87.83
18.721
13.91
5.90
1.10
Case-5
8.799
5.105
15
2.28
104.84
35.184
8.74
4.35
1.72
Table
18 9
Economic Evaluation Results
The very nature of economic evaluation entails risk taking and uncer-
tainties involving technical, economic, and political conditions. Th e results
of the analysis are subjected to many restrictive assumptions in forecasting
recoveries, oil and
gas
prices, investment and opera ting costs, and inflation
rate. Unforeseen national and w orld economic and political climates can also
severely effect the outcome of the projects.
Figure 18-17 shows the sensitivities of DCFR OI and
PWNP
to the oil
price, oil production, investment, and operating costs. The analysis shows
tha t D CFR OI is affected more drastically by oil price, oil production, and
investment than by the opera ting costs.
PWNP
is most sensitive to oil price
and oil production. Note that the data points for operating costs and capital
investments are nearly coincident in this p lot.
231
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DCFROI
Sensit iv i ty Analysis
45-
40
3 5 -
3 30
25 -
20 -
2 15
10
-
5.
y1
180 1
Oil
Price or
. Production
+Operating Costs
+Capital investment
+Oil Price
or
Production
0 100 +Operating Costs
X
,
, , ,
, ,
+--CapitalInvestmeni
20
0
-0.30
0.20 0.10
0.00
0.10 0.20
0.30
Percent Change from Base Case
- . . . . .
. .
0.30 0.20 0.10
0.00 0.10
0.20
0.30
Percent Change from Base Case
Fig. 18 17 Sensit iv i ty A nalysis of Case 2
REFERENCES
1.
Satter, A., and Thakur, G. C:
Integrated Reservoir Management
A Team Approach, PennWell Books, Tdsa, O K 1994)
232
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Chapter V I N
E
T E E N
M I N I S I
U L A T I O N
r X A M P L E S
I N T R O D U C T I O N
Historically, reservoir simulators have
been used for studying fdl-field produc-
tion performance. These studies are cost-
ly, requiring highly trained professionals,
and are often too time-consuming for
operating department environments.
Simulators are now available on personal
computer platforms to allow access to a
single most versatile tool-mini-simula-
tion.
Mini-simulation can play an impor-
tant role in everyday operations, such
as:
single vertical well performance
single horizontal well performance
single well coning performance
pattern waterflood performance
This chapter will be devoted to appli-
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5 spot
WateIf lOal
uodsl
Fig.
19-1
Merlin Reservoir Simulation and Management Wizard
cations of mini-simulation using a commercial black oil simulator. The
influence of various factors on recovery performance will be analyzed. The
approach to the sensitivity study will be to build a base case and then to ana-
lyze
the performance
as
a specific parameter is varied. Understanding the
effects of these factors on recoveries is essential in designing, implementing,
monitoring, and evaluating the project. In order to demonstrate the mini-
simulations, a reservoir simulation program known as MERLIN (from
Gemini Solutions) is used (Fig. 19-1).
For each of these mini-simulations, the same basic rock, fluid, and reser-
voir description data were used. Fluid data for oil and gas were based upon
correlations using basic input reservoir temperature, oil and gas specific grav-
ity, rock compressibility, initial solution GOR, and others:
Reservoir temperature . . . . . . . . . . . . . . .123 F
Rock compressibility
3.0
E-OG/psi
Oil density ..................... .33 API
234
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Gas specific gravity
.
.
.
0.6773
Initial solution G O R . . . .
.350
tb/scf
Bubble point
. . . . 1,855
psia
Oil viscosity . .
. . . 1.56
cp
General oil PVT properties as calculatedby the Merlin pre-processor are
shown in Figure
19-2,
nd the
gas PVT
properties are shown in Figure
19-
3.
Relative permeability data were developed using correlations and basic sat-
uration endpoints as shown below:
Conna te water saturation,
Sw
. . . . . 0.220
Residual oil saturation to gas,
So
. .
0.010
Residual oil saturation to water,
So,
. 0.200
Critical
gas
saturation,
SF .
0.020
Relative permeability to water, K,
. . . 1
OOO
Relative permeability to oil in water,
K
0.350
Relative permeability to oil,
K,
.
.
.
1.000
Relative permeability to oil in gas,
Kv
. .
1.000
Fig. 19-2 Merlin PVT Oil Properties
235
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E S
ER
V O
I
R
dl
NNAGEMEN T
P U T E R A S S S T E D
Fig. 19-3 Merlin
PVT
Gas Properties
Fig. 194 Merlin Oil- Water and Gas-Oil Permeability Correlation
236
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Figure 19-4 shows the oil-water and gas-oil two-phase relative perme-
ability curves developed by the Merlin simulator.
All of the mini-simulations used the same general reservoir description
for a 5-layer model. The changes were in drainage area, simulation grid
dimensions, and/or radial vs. Cartesian coordinates. These properties are
summarized in Figure 19-5. The vertical permeability
K )
s set at 0.10
times horizontal permeability
45 .
SINGLE VERT IC L
W E L L
Production performance of a vertical well can be analyzed with a mini-
simulation model to show the effects of drainage radius, layering, comple-
tion, rock, and fluid properties. This study will be very useful in case
of
a
new discovery with limited data available. A radial model was developed for
an oil well in the center
of
the field, which is completed in
all
five layers. The
model grid was radial and dimensioned 10x5. There was no oil/water con-
tact and the reservoir was initially above the bubble point (no gas cap). This
Fig.
19-5
Merlin Grid Data
237
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example varied only drainage radius from 160 acres to 640 acres. In-place
volumes were as follows:
Case 1
Case
2
Drainage area
160 acres 640 acres
Oil
-
MMSTB 3.67 14.69
Gas
-
BCF 1.29 5.14
For both models, the production well was initialized at an oil rate
of
1,000 bbldday. Figure 19-6 plots oil rate in barrels/day. It shows, while both
wells can make the initial oil rate, performance for the 160-acre drainage
area has a rapid deterioration. This is because the larger drainage area allows
higher sustained rates under
a
normal depletion drive reservoir mechanism.
Figure 19-7 plots GOR. In the case with the smaller drainage area, it shows
that gas breakthrough is observed at about 1,200 days as a result of the for-
mation of a secondary gas
cap. Figure 19-8 plots average reservoir pressure
and shows when each case drops below the bubble point. This occurs at
Single Well Model
Drainage Area Variation
1000
800
0
0
500
1000
1500
Zoo0
2500
3000
3500
4
Time :
DAYS
60 Acre Drainage 40Acre Drainage
Fig. 19-6 Predicted Vertical Well Oil Production
238
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approximately 147 days for the 160-acre drainage case versus
548
days for
640-acre drainage. These figures show how a well in a larger drainage pat-
tern would perform in contrast
to
a smaller drainage pattern.
As
observed,
the larger drainage area allows for higher rates, more recovery, and slower
decline in overall reservoir pressure.
At the end of a 10-year production period, we have the following
comparison:
160
Acres
Initial reservoir pressure, psia 2,337
Final reservoir pressure, psia 1,235
Initial oil rate, b/d 1,000
Final oil rate, b/d 95
Final
gas
rate, mcf/d 115
Cum. oil produced, mstb 521.3
Cum.
gas
produced, mmscf 237.0
Single Well Mo del
Drainage A rea Variat ion
640
Acres
2,337
1,672
1,000
155
49
666.5
208.0
500
0
0 500 1000
1500 2500
3wo 3500
4000
Time
:
DAYS
-160 Acre Drainage 4
Acre
Drainage
Fig. 19-7 Predicted Vertical Well Gas-Oil Ratio
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Slngle Well
Model
Drainage Area Variation
Drop BelowbubblePoint 1,855 P W
0
0 5m 1 M O 1500
2MIo
2
3wo
3500 4ow
Tim
: AYS
-160 Acre Drainage O Acre Drainage
Fig. 19-8 Predicted Vertical Well Reservoir Pressure
SIN GLE LAT ERA L HORIZONTAL
W E L L
Potential production performance of a horizontal well can be analyzed
to show the effects
of
well leng th, zone thickness, vertical-to-horizontal per-
meability ratio, and the position within the pay interval,
as
well as distance
from an oil/water or gas/oil contact.
A
sensitivity study could also be made
to evaluate the effects of aquifer influx.
A
simple model was set up to dem on-
strate the analysis of horizontal wells by com paring variations in well length.
All properties are the same
as
in the other examples. This model has grid
dimensions of
21x21~5
or a total of
2 205
cells, with each cell
528 fi by
528 fi in length.
This example has five layers:
Layer Elevation,
i Thickness, fi
FluidType
1 -5 332
5 Oil
2
-5 337
5 Oil
3 -5 342 5
Oil
4 -5 347 5
Water
5 -5 352 5 Water
240
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I N I - S I
U L A T I O N
A M P L E
There is an oil/water contact at
-5 347
at the top of layer
4 .
The reser-
voir is above the bubble point, and there is no
gas
cap or active aquifer. The
horizontal well is centered within the grid as shown in Figure
19-9.
In-place
volumes are
as
follows:
Oil, mmstb ....................... . 34 .6
Water, mmstb
. 44 .2
Gas
cf
........................... 12.1
Free
gas
bcf 0.00
Initial pressure, psia
. . . . . . . . . . . . . . . . .
335
The horizontal well was completed in the middle of layer 2 and was
assigned an initial rate of 2 000 bbldday
of oil.
Horizontal well length was
the only variable that was investigated, with three variations in length.
Simulation runs were over a 10-year period and are summarized below:
Run A
B
Horizontal length,
ft
3,800
2 900 1 850
Final pressure, psia 1 469
1 521 1 587
Final oil, b/d
211
190 162
Final gas mcf/d
149 116 82
Final water, b/d
386
354 307
Final GOR
707
607 504
Final water cut 0.64
0.66 0.66
Cumulative oil, mstb
1 528.7 1 278.7 998.2
Cumulativegas mmscf 662.7
517.1 368.3
Cumulative water, mstb
1 952.5
1 736.4
1 440.8
Horizontal well - ength is varied
Fig. 79-9 Hor izon tal Well Example-Areal view
241
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This group of runs shows how recovery is affected by the length of the
horizontal well-all other factors remaining the same. Figure
19- 0
plots
oil
rate vs. time. It clearly shows the longer horizontal well
c n
sustain the ini-
tial
2,000
bbl/day oil rate for a longer period of time.
As
might be expected,
cumulative oil production was greater for the well with the horizontal length
of 3,800 i At 1,528.7MSTB, it was 53 greater than the oil recovery for
the 1,850 i well length.
Figure
19-1
plots water cut fraction versus time and shows the well
with the longer horizontal length has a lower overall water cut. This is due
to the overall lower pressure drop attributed to the longer horizontal well
length for the same initial oil rate. Water break-through occurs at
50
days
for the horizontal length of 1,850 i, 71 days for a length of 2,900 t, and
147 days for the length of 3,800 i The shorter the horizontal length, the
greater the overall pressure drawdown and thus a greater tendency for
water to cone.
Figure
19-12
elates
GOR
s. time for the three horizontal lengths.
As
a
result of production, overall reservoir pressure is dropped and a secondary
gas cap is formed.Gas break-through occurs at
44
days for the shortest well
Horizontal Well Model
0
0 lmfi
3wo
T i m :
DAYS
- R u n A - Ru n 6
- -RunC
Fig. 19-70 Predicted Horizon tal Well Oil Produc tion Rate
242
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M I N I - S I
U L A T I O N
A M P L E
Horizontal
Well Model
0.800
0 W
0
1000
2000
Time : AYS
-RunA -----RunB Run C
3W0
Fig. 19-11 Predicted Horizontal Well Water
Cuf
Horizontal
Well Model
1WO
m
800
3
200
100
0
1000 MW
Time
:
DAYS
-RunA -RunB --RunC
3WO
Fig. 19-12 Predicted Horizontal Well Gas-Oil Ratio
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length (1,850
ft
to 125
days
for the 3,800
ft
length. Figure 19-13 relates
average reservoir pressure and shows that the greatest drawdown occurs for
the 3,800
f t
length that has the greatest amount of production.
Other variables related to horizontal well performance that can be
evaluated are:
initial oil rate
distance from any oil-water or gas-oil contacts
strength of aquifer influx
relative permeability end points and curvature
well
completion efficiency
Economic analysis always needs to be applied to determine if the addi-
tional oil recovery
as
a result of additional horizontal well length is worth the
incremental drilling cost.
HorizontalWell
Model
2500
4
B
tow
0
' two
Mw
Time :
DAYS
- Ru n A - R u n 8 - - R u n C
Fig.
19-13 Predicted Horizontal Well Reservoir Pressure
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M I N I - S I U L A T I O N
A M P L E
SINGLE
W E L L W A T E R
CONING
Production performance of a well possibly subject to water coning can
be analyzed to study the effects of completion interval, vertical to horizontal
permeability ratio, production rate, drainage area
as
well as relative perme-
ability effects. This example uses a single well radial model to evaluate com-
pletion interval and tendency to cone water. All rock and fluid properties are
the same
as
in previous examples.
There are five equal layers, each five feet in gross thickness with a
net/gross ratio = 1.0. As shown in Figure 19-14, the well is in the center of
the radial drainage area and can be completed in any or all of the five layers.
There was an oil/water contact located at the interface of layers
3
and
4
that
resulted in the model having 10 feet below the oil/water contact and 15 feet
above the contact. This mini-simulation looked at the effects of a well com-
pleted in the following layers:
Run
Layers
A 1-5
B
1-4
C 1-3
5 f t O f l
5 ft i l
5 ft. oil
5
ft. water
5 ft. water
-
- pen perforations
Layer 1
Layer 2
Layer 3
Layer
4
Layer 5
Fig.
19-14
Producing Well Radial Drainage Area
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The radial drainage area was set to be 160 acres with in-place volumes
as follows:
Oil, mmstb .......................... .2.16
Water, mmstb
. . . . . . . . . . . . . . . . . . . . . . . .
.2.76
Gas bcf 0.76
Free
Gas
bcf
..........................
0.00
Initial Pressure, psia
..................... 2,335
Figure
19-15
shows the dimensions of the radial model as 10x5 for a
total of
50
grid cells, and also shows how the radial spacing was set up.
The well was set to have an initial oil production rate of
200
bbldday.
Variations in com pletion interval were investigated. Add itional runs could
be m ade to evaluate op timum producing rate for a given com pletion inter-
val. Figure 19-16 plots the oil rate and shows tha t a higher oil rate can be
sustained with a longer completion interval, bu t after 10 years of produc-
tion, the final oil rate is essentially the same fo r all three cases. A w ater-cut
I
Fig. 19-15 Radial Model for Coning
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plot in Figure
19-17
shows
that
all
cases initially produce at
high
water cuts
ie., a significant tendency to cone. The completion interval over all five lay-
Single Well Model
Completion Variation
m
0
0 500 1009 m 3wo 3500
4wo
r ime
:
AYS
-RunA -Run6 ---RunC
Fig. 19-16 Coning Well Oil Production
Single Well Model
Completion Variation
0.900
0.800
0 m
i.
0,400
li
E 0 m
0 m
0.100
0.030
0 500
1Mo 1500 2 m NO0
3 m
4000
nm :DAYS
-Run A -Run 6 ---RunC
Fig. 19-17 Coning Well Wafer Cut
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ers (including
2
ayers below the oil/water contact) had the highest initial
water cut and remained the highest during the 10 year producing period.
The lowest water cut was achieved with the completion interval only in the
three layers above the oil/water contact. Figure 19-18 s a GOR plot which
shows that Run
A
(completed in all five layers) has a large increase in gas pro-
duction at approximately
GOO
days. Due to large amounts of oil and water
production, the reservoir has fallen below the bubble point, and a secondary
gas
cap has been formed, resulting in free
gas
production. Figure 19-19 lots
average reservoir pressure for
each
of the three runs. The change in slope is
indicative of the reservoir dropping below the bubble point of 1,855psia.
The table below summarizes the results from this series of runs:
Run A Run B Run
Initial oil rate, bbls/day 200
200
200
Final reservoir pressure, psia
743
1,120 1,794
Final
oil
rate, bbldday
16.8 16.8 18.2
Final
gas
rate, mscf/day
111.3 466
16.1
Single Well Model
Completion Variation
8
0
0
5w 1Mo 1500 2wo
25w 3Mo
3500
n m
AYS
-RunA
-Run6
---RunC
Fig.
19-18
Coning Well Gas-Oil Ratio
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Final water rate, bbldday 86.4 62.9 60.6
Cum. oil production, mstb 163.2 125.7 100.3
Cum. water production, mstb 262.5
81.8
37.8
Cum.
gas
production, mmscf 587.9
375.6
275.7
Other variations can be made using this type of model to evaluate possi-
ble water or
gas
coning. The effects of vertical permeability,
as
well
as
the
effects of the actual relative permeability curves (end points and curvature),
could be studied
as
a sensitivity. The overall goal would be to optimize recov-
ery with minimal coning. This sort of analysiscan be important if production
facilities have limited water handling capacity or if water disposal is costly.
5 - S P O T
W A T E R F L O O D I N G
An
in-depth analysis of waterflood recoveries
is
influenced by
fictorssuch as:
timing of flood
layering attributed to depositional environment
Single Well Model
Completion Variation
Bubble Point
Fig.
i9-i9
Average Reservok Pressure
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vertical permeability variation
free gas saturation
cross flow due to vertical permeability variation
oil gravity
voidage replacement ratio
producer/injector ratio
injection pattern
increased pattern density
Initial runs were made for a 160-acre 5-spot pattern in an 11x11x5 sim-
ulation grid, resulting in a simple model
of
605 cells. The injection wells
were on the corners and the production well was in the center, as shown in
Figure 19-20. This reservoir had no oil/water contact and the pressure was
above the bubble point (no initial
gas
cap) with in-place volumes as follows:
Oil, mmstb 3.67
Water, mmstb
................
.12
Gas
cf
.....................
1.29
Free
gas,
bcf
. . . . . . . . . . . . . . . . . . .00
Initial pressure, psia
. . . . . . . . . . .
,335
Fig.
19-20 5-Spot
Waterflood Model
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The first run was straight depletion with no pressure maintenance and
production from the single producer in the center of the field. The initial oil-
producing rate was set at
500
bbls/day. The second run initiated water injec-
tion at approximately 4,745 days (13 years), where the reservoir pressure had
declined to 717 psia. Water was injected at the four corners in a typical 5-
spot operation at a maximum injection pressure of 2,300 psia. Pattern oil
recovery was 18 under straight depletion and 45 with water injection.
The 45 recovery was achieved after a total of 20,000 days of production
and 15,255 days of water injection. Figure 19-21 plots oil rate vs. time. This
plot shows after water injection is initiated, approximately 2,000 days are
required for fill-up to occur and for oil production to increase.
Figure 19-22 plots produced water rate. There is no water production
until 10,585 days or 5,840 days after water injection has started. Figure 19-
23 plots average reservoir pressure. Once fill-up has occurred, reservoir pres-
sure increases, since total volume of water injected is greater than the reser-
voir voidage from production.
One obvious conclusion from this group of runs is that, while a 45
recovery factor from water injection is quite good, the production time
500
4
Start ofwater injection
Waterflood Response
0
0
XXK
m
15oM)
Time
:DAYS
-Depletion .LWaterFlood
Fig. 19-21 5 0t Waterflood Oil Production Rate
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200
150
Water
Break-through
50
0
0
IOOOO
Time
:
DAYS
-Depletion IaterFlood
15000
Fig. 19-22 5-Spot Waterflood Water Production Rate
2500
2OOO
1500
f
f
t
1000
LL
500
0
0 s w o
Start
of Water In ject ion
l o w0
Time
:
DAYS
epletion aterFlood
15000
2wM)
Fig. 19-23 5-Spot Waterflood Reservoir Pressure
2 5 2
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M I N I - S I U L A T I O N
A M P L E
frame of some
20 000
days is much too long. One single 5-spot pattern is
not adequate for the timely sweep of a 160-acre drainage area. Increased
pattern density is indicated. With these typesof
models, additional patterns
can
be incorporated using increased grid density. For a more in-depth
example
of
a waterflood pattern development, refer to chapters
5-7
of
Thakur and Satter.'
R E F E R E N C E S
1. Thakur, G. C., and Satter, A.:
Integrated WaterjZoodAssetManagement
PennWell Books,Tulsa, Oklahoma (1998)
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igure
2 1
2 2
2 3
2 4
2 5
2 6
2 7
2 8
2 9
2 10
2 1 1
igure 3 1
igure
4 1
igure 5 1
5 2
5 3
5 4
5 5
5 6
5 7
igure
6 1
6 2
6 3
6 4
6 5
6 6
6 7
6 8
6 9
6 10
4 2
igure 7 1
Reservoir Life Process
7
Integration of Geoscience and Engineering. . . . . . . . . .
8
Multi-Disciplinary Reservoir Management Team
Old New Organization Styles
. . . . . . . . . . . . . . . . 10
Texaco
E
P
Technology Organization
. . . . . . . . . . . 11
Reservoir Management Process 2
What is Reservoir Management?
. . . . . . . . . . . . . . . .
3
Developing Plan ............................
14
Reservoir Knowledge......................... 15
Production and Reserves Forecast. . . . . . . . . . . . . . . . 6
Performance Evaluation.......................
17
Data Acquisition and Analysis . . . . . . . . . . . . . . . . . . 2
Integrated Reservoir Model
....................
33
Data Sharing and Validation 4
Entry of Logging Information
. . . . . . . . . . . . . . . . ..
5
Raw Logs Ready for Analysis 6
Analysis Plots for Lithology Parameters. . . . . . . . . . .. 7
Results of Log Analysis ....................... 48
Log
Cross Section
...........................
49
Summation Results Table...................... 50
Posted Porosity Values from Logs
. . . . . . . . . . . . . . . .
1
The Seismic Record Section
....................
54
Seismic Reflection Amplitude
. . . . . . . . . . . . . . . . . . 5
Seismic Amplitude 56
Vertical Resolution
.......................... 57
Migration ................................. 58
Seismic Cross Section ........................
59
Seismic Interpretation
........................ 60
Time Migration vs Depth Migration
. . . . . . . . . . . . .
61
Seismic Amplitude Map 62
3-D
Visualization ........................... 63
Base Map with Structure Control . . . . . . . . . . . . . . . . 7
X I
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7 2
7-3
7 4
Figure 8 1
8 2
8-3
8 4
8 5
8 6
8 7
8 8
8 9
8 10
8 11
8 12
8-13
8 14
8 15
8 16
8 17
8 18
8 19
Figure 9 1
9 2
9 3
9 4
9 5
9 6
9 7
9 8
9 9
Figure 10 1
10 2
10-3
10 4
Computed Structure Contours
. . . . . . . . . . . . . . . . . .
8
3-D Mesh Plot ............................. 69
4-D
Mesh Plot 70
Reservoir Properties Between the Wells? . . . . . . . . . . . 4
Well Logs Providing Reservoir Properties . . . . . . . . . .75
Conventional Contouring
..................... 77
Spatial Correlation of Data 78
Variogram .................................
80
Variogram Terminology ....................... 81
Directional Variograms ....................... 82
Variogram Ellipse
............................ 83
Kriged Map (Contoured)
...................... 84
Kriging Reflects Data Trends . . . . . . . . . . . . . . . . . . .
Kriged Map (Color filled) .....................
86
Uncertainty in Kriging
........................ 87
Realization Comparisons 88
Predicted Net Thickness.......................
89
Probability Plots for Proposed Well Location. . . . . . . .
90
Probability of Net Thickness > 125 feet
90
3-Dimensional Temperature Model . . . . . . . . . . . . . . 1
Drawdown and Buildup
.......................
94
Drawdown Curve ........................... 98
Example Buildup Curves Illustrating Various Effects
. .
100
Type Curve for Wellbore Storage Skin
. . . . . . . . .
01
Pressure Data for Example Problem
. . . . . . . . . . . . . 03
Log-Log
Plot of Build-up Pressure for Example Problem
.
104
Initial Type-Curve Match for Example Problem . . . . 04
Horner Plot............................... 106
Simulated Pressure Histories for Example Problems 11
1
Hydrocarbon Phase Behavior
. . . . . . . . . . . . . . . . . .
16
Hydrocarbon Reservoirs
...................... 117
Efficiency ................................ 118
Salt Wash Net Thickness Measured at Wells . . . . . . . . 6
Calculating a Variogram
79
Effects of Reservoir Producing Mechanisms on Recovery
Definition of Reserve........................ 120
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igure
11 1
11 2
11 3
igure
12 1
12 2
12 3
12 4
12 5
12 6
12 7
12 8
12 9
igure
13 1
13 2
13 3
13 4
13 5
igure 14 1
14 2
14 3
14 4
igure 15 1
14 5
15 2
15 3
15 4
15 5
15 6
15 7
15 8
15 9
15 10
15 11
15 12
15 13
15 14
Subsea Depth for Determining Reservoir Volume
. . .
123
Example Net Pay Isopach
..................... 124
Plot of Contour Area vs Contour Interval 125
Decline Curve Predictions
.................... 130
Production Rate Plots ....................... 131
Log
of Water Cut vs
.
Cumulative Oil Production . . . 132
Decline Curve Methods
......................
133
Oil. Water and Gas Production . . . . . . . . . . . . . . . .
136
Oil Rate History Match and Prediction
137
Water Cut History Match and Prediction . . . . . . . . .137
Recovery vs
X .............................
139
Recovery vs
.
Water Cut
...................... 139
Pressure Data for Case Study
..................
46
Production Rates for Case Study . . . . . . . . . . . . . . .
146
Water Cut and GOR for Case Study. . . . . . . . . . . . .147
Drive Mechanism Plot
.......................
148
Regression on Pressure History
. . . . . . . . . . . . . . . . .
149
Typical Simulation Models 153
Reservoir Simulation ........................ 155
Pressure Match Procedure
157
Saturation Match Procedure. . . . . . . . . . . . . . . . . . . 58
Productivity Match Procedure . . . . . . . . . . . . . . . . . 159
Texaco EPTD Computing Environment
. . . . . . . . . .166
Scientific Sobare-Intercomp (SSI) . . . . . . . . . . . . . 169
KAPPA
Engineering 169
Fekete Associates
170
Beicip-Franlab............................. 171
Landmark Graphics ......................... 172
U.S.
Department of Energy
. . . . . . . . . . . . . . . . . . .173
Mer k . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
SPE Gulf Coast Section ...................... 174
SPE International
175
Halliburton 175
Schlumberger ............................. 176
Petrotechnical Open Sobare Corporation (POSC) 177
Computer Modeling Group (CMG). . . . . . . . . . . ..171
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15 15
15-16
15-17
Figure
16-1
16-2
16-3
16-4
16-5
16-6
16-7
Figure
17-1
17-2
17-3
17-4
17 5
17-6
17-7
17-8
17-9
17-10
17-11
17-12
17-13
17-14
17-15
Figure
18-1
18-2
18-3
18-4
18-5
18-6
18-7
18-8
18-9
18-10
Federal Energy Technology Center (FETC)
. . . . . . . .
177
Edinburgh Petroleum Services (EPS)
. . . . . . . . . . . . . 178
Petroleum Technology Transfer Council (PTTC)
. . . .
179
4000 fi
Sand Structure Map . . . . . . . . . . . . . . . . . .
82
4500 fi
Sand Structure Map . . . . . . . . . . . . . . . . . .
183
Effect of Well Spacing on Recovery
. . . . . . . . . . . . . . 89
Pressure and Cumulative Production vs.Time. . . . . .
189
Effect of Water
Cut
and GOR vs Time . . . . . . . . . .
90
Effect
of
Aquifer Size on Rate and Cumulative
Production ...............................
190
Cumulative Oil Production vs Time
. . . . . . . . . . . . 191
North ApoilFuniwa Life Cycle
. . . . . . . . . . . . . . . . . 96
North Apoi/Funiwa Field Location Map . . . . . . . . . .
196
North Apoi/Funiwa Field Seismic Line . . . . . . . . . . .
98
North ApoilFuniwa Stratigraphic Cross-Section . . . .
198
North Apoi Well 34
Log
and Core Data
. . . . . . . .
99
Ewinti and Ala Reserves
as
of December
3 1. 1994) 200
North Apoi/Funiwa Field
197
Integration/Alliance.........................
200
Organizational Alliance
...................... 201
Data
nd
Sohare Integration
02
Integration of Professionals
................... 203
Original Oil in Place
........................ 206
Reserves Addition Summary
. . . . . . . . . . . . . . . . . ..
08
Top Structure Map
......................... 214
Gross Thickness-Layer
1
.................... 215
Net Thickness-Layer
1
......................
218
Porosity-Layer 1
.......................... 218
Permeability-Layer
1 ....................... 219
Cross Section. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
220
Oil Properties
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
220
as
Properties
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Gas-Oil Relative Permeability. . . . . . . . . . . . . . . . . .
22
Water-Oil Relative Permeability . . . . . . . . . . . . . . . .
22
Integration of Technologies 05
Oil Saturation Distribution
. . . . . . . . . . . . . . . . . .. 09
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18-1 1
18-12
Fieldwide Primary Production Rates and
Pressure Behavior
...........................
223
Fieldwide Primary Cumulative Oil. Water.
and Gas Productions ........................ 224
18-13 Development Cases .........................
224
Cumulative Oil Recovery vs Time . . . . . . . . . . . . . . 226
Oil Recovery vs Water Injected
. . . . . . . . . . . . . . . .
26
Economic Optimization...................... 227
Sensitivity Analysis
of
Case 2
. . . . . . . . . . . . . . . . . .
32
Merlin PVT Oil Properties
....................
235
Merlin PVT
Gas Properties
. . . . . . . . . . . . . . . . . . .
36
18-14
18-15
18-1
6
18-17
igure 19-1
19-2
19-3
19-4 Merlin Oil-Water and Gas-Oil Permeability Correlation 236
19-5 Merlin Grid Data .......................... 237
19-6 Predicted Vertical Well Oil Production .......... . 38
19-7 Predicted Vertical Well Gas-Oil Ratio . . . . . . . . . . . .
239
19-8 Predicted Vertical Well Reservoir Pressure
. . . . . . . . .
40
19-9 Horizontal Well Example-ib e view . . . . . . . . . . . 41
19-10 Predicted Horizontal Well Oil Production Rate
.....
42
19-1
1
Predicted Horizontal Well Water Cut . . . . . . . . . . . . 43
19-12 Predicted Horizontal Well Gas-Oil Ratio . . . . . . . . . 43
19-13 Predicted Horizontal Well Reservoir Pressure
. . . . . . .
44
19-14 Producing Well Radial Drainage Area . . . . . . . . . . . . 45
19-15 Radial Model
or
Coning ..................... 246
19-16 Coning Well Oil Production
. . . . . . . . . . . . . . . . . .
47
19-17 Coning Well Water Cut ...................... 247
19-18 Coning Well Gas-Oil Ratio . . . . . . . . . . . . . . . . . . .248
19-19 Average Reservoir Pressure
....................
249
19-20 5-Spot Waterflood Model..................... 250
19-21 5-Spot Waterflood Oil Production Rate . . . . . . . . . . 51
19-22 5-Spot Waterflood Water Production Rate . . . . . . . . 52
19-23 5-Spot Waterflood Reservoir Pressure . . . . . . . . . . . . 52
Merlin Reservoir Simulation
and
ManagementW i d 234
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Table
3 1 Reservoir Data Classification 23
Table
4 1 Data Sources 31
Table
9 1 Reservoir Data for Example Problem 103
Example Problem 105
Horner Calculations for Example Problem 07
Check of Interpretation Consistency 107
9 2
9 3
9 4
Log Log Type Curve Calculations for
Table
10 1
Table
15 1
15 2
15 3
1 5 4
Table
16 1
16 2
16 3
Table
16 4
Table
17 1
17 2
17 3
17 4
Table
18 1
18 2
18 3
18 4
18 5
18 6
18 7
18 8
18 9
Comparison
of
Reservoir Performance Analysis/
Reserves Estimation Techniques 119
Stand alone Software Packages 63
Integrated Sohare Packages 164
Energy related Web Sites 168
Wizard Field Layer Data 186
Reservoir Computing Facility Installed Applications 167
Wizard Field Data nd Sources 85
Wizard Field Drilling. Completion.
and Production Schedule 187
Wizard Field Economic Evaluation 188
North ApoiIFuniwa Field General Data 200
Team Members 204
Data Sources 204
Funiwa Ewinti 5 Reservoir Horizontal Well esults 210
Reservoir Description: Layer No 1 216
Reservoir Description: Layer
o
2 216
Reservoir Description: Layer o 3 216
Reservoir Description: Layer No 217
Reservoir Description: Layer
No 5
217
Produced Oil Volumes 225
Economic Data 228
Economic Evaluation of Case 2 230
Economic Evaluation Results 31
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This page has been reformatted by Knovel to provide easier navigation.
INDEX
Index Terms Links
#
2-D mapping 87
3-D View 172
3-D display 69
3-D extension 87-89 91
3-D mapping 87-88
3-D mesh plot 69
3-D model 91
3-D seismic data 25 30-31 53-54
70-71
interpretation 70-71
3-D visualization 62-64 70-71 206
4-D display 70
4-D mesh plot 70
A
Abandonment 7
Acoustic impedance 62
Acoustic velocity log 37 41-42
Ala series 195 200
Algorithm 76 80 85
Alternative displays (data) 69-71
Analysis procedure (decline curve) 136
Analytical estimates 117-119
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Index Terms Links
This page has been reformatted by Knovel to provide easier navigation.
Approach (field study) 201
Auto-pick 63-64
B
Baker-Hughes/SSI 43-51 66-71 109-113
136 160 168-169
203 210 217
Base map 67
Before federal income tax 229
Beicip-Franlab 171
Black oil simulator 17 152 154
160 186-191 205-206
217 219 221
225-224 233-253
Buildup analysis 94-95 98-100
C
Caliper log 38-39
Campbell method 143
Case studies 144-149 181-193 195-211
213-232
material balance 144-149
new field development plan 181-193
mature field study 195-211
waterflood project development 213-232
Challenges (field study) 199
Checks during analysis (well test) 108-109
Chemical simulator 155
Classical material balance 117 119 205
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Cole method 144
Color map values 81 86
Color-fill displays 69
Combination porosity log 42-43
Common platform (software) 35
Common reflection point 55
Communication (database) 25
Compositional simulator 17 154
Computer Modeling Group 170-171
Computer software xix-xx 1-4 161-179
stand-alone software 162-163
integrated software packages 162-164
Petrotechnical Open Software Corporation 164-165 176-177
computing facilities 165-167
internet 165-166 168
sources 164-165 168-179
Computerized mapping 65-71
Computing facilities 165-167
Conditional simulation 82
Conservation of mass 95 153
Constant rate curve 130 134-135
Constant terminal rate solution 96-97
Contouring 66 69 76-77
Contouring algorithm 66
Contrast (acoustic impedance) 55
Control contours 66
Control data 66 69
Control points 66 76
Conventional analysis (well test) 96-99
Conventional mapping 75-77
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Conventional well test analysis 96-100
drawdown 97-98
buildup 98-100
Core analysis 26 30 195
199
data 195 199
Correlation (data) 31 78
Cross section 46 49 75
220
Cumulative density function 86
Cumulative probability distribution 86
Cumulative production plot 132
D
Darcy’s law 95-96 153
Data acquisition/analysis 4 21-27
data types 22-23
acquisitionlanalysis program 23-24
data validation 24
data storing/retrieval 24-25
data application 25-27
Data analysis (well log) 44-45 47-48
Data application 25-27
Data entry 44-45 66
well log 44-45
Data integration (modeling) 32-35
Data interrogation 76-77Data management 4 21-27
Data preparation (well log) 44 46
Data sources 30-31
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Data storing/retrieval 24-25
Data trends 85
Data types 22-23
Data validation 24 108
Data visualization 65-71
control data 66
data entry 66
software 66-69
visualization example 66-69
model data 68-69
alternative displays 69-71
Database 24-25
Decline curve 27 117 119
129-140 203
method 129-140
equations 132-135
analysis 203
Decline curve equations 132-135
Decline curve method 129-140
decline curves 130-132
decline curve equations 132-135
analysis procedure 136
software 136-440
example analysis 136-140
standard method 136-138
Ershaghi and Omoregie method 138-140
Definitions 6 118-120
reservoir management 6
reserve 118-120
Delineation 7
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Deliverables (field study) 200-205
Density log 38 42
Depletion strategy 184 187
Depth migration 58
Desktop VIP 172
Development (field) 7
Development strategy 184 221 223
225-226
Diffusivity equation 95-96
Digitizing 66
Dimensional model 240-244
Dimensionless parameters 101-102
Discontinuity 80
Discounted cash flow return on investment 228
Discovery 7
Discretization 152
Display items control 67-69
Distance-direction variation 79
Drawdown analysis 94-95 97-98
Drawdown curve 97-98
Drillstem test 94-95
Dual induction log 41
E
Economic data 228
Economic evaluation 188 192 225
227-232 Economic optimization 14-18 192
Edinburgh Petroleum Services 178
Electric log 41
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EMERAUDE 169
Engineering role (modeling) 31-33
Enhanced oil recovery 17 23 199
Enhanced oil recovery simulator 17
Equation of state 24 95
Ershaghi and Omoregie method 138-140
Evaluation 18 193
reservoir management 18
field development 193
Ewinti series 195 200
Example analysis 43-51 66-69 102-107
136-140 144-149 213–232
well log 43-51
mapping 66-69
visualization 66-69
type curve 102-107
well test 102-107
decline curve 136-140
material balance 144-149
waterflood 144-149
Exploration 7
Exponential decline curve 130-131 133-135
Extrapolated pressure 99
F
Facilities planning 191
Falloff test 94-95False pressure 99
Faulted structural trap 182-183 185
Faults 66
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FE method 143
Federal Energy Technology Center 176-177
Feedback 30
Fekete Associates 170
Field data 186 200
layer 186
Field description 195-200
Field development plan 181-193
Wizard field example 181-193
development strategy 184
depletion strategy 184
reservoir data 182-183 185-186
reservoir modeling 186-187
production rate forecast 187-191
reserve forecast 187-191
facility planning 191
economic optimization 192
implementation 192-193
monitoring/surveillance 193
evaluation 193
Field facilities 18
Field recording 58
Field study 4 195-211
File import 66
Finite difference 154
Five-spot waterflooding 249-253
Flow phase/direction 152-153
Fluid contact 31
Fluid data 26
Fluid distribution 32 71
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Fluid flow equations 153-154
Fluid property 23-24 26 108
Fluid saturation 31 38
Forecasts 16-17 118-120 187-191
221 223-226
Formation evaluation 38
Formation thickness 75-77
Formation top 31
Formation volume factor 126
Free gaslgasgap 126
G
Gamma ray log 37 40
Gas cap drive 142
Gas cap method 143
Gas recovery factor 127
Gas reservoir 115-117 143-144
Gemini Solutions 234
Geologic model 16
Geologic trend 74 76-77
Geological data 23
Geological map 25
Geology 33
Geometry (seismic ray path) 57
Geophysical model 16
Geophysics 33
Geoscience role (modeling) 30-32
Geostatistical analysis 73-92
conventional mapping 75-77
map statistics 76-83
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Geostatistical analysis (Cont .)
variogram 79-83
kriging 80-81 84-87
simulation 81-84 88
measuring uncertainty 85-87 89-90
3-D extension 87-89 91
model 88 91
Geostatistical model 88 91
Geostatistics 32-33 73-92
Goal setting 13-14
Graphical integration 124-126
Graphical method 144
Grid cell 68 86
cell values 68
Grid model 187
Gross thickness 75 215-217
Guard log (laterolog) 41
H
Halliburton 174-175
Hand contouring 76
Hard data 74 88-89
Harmonic decline curve 131 133 135
Havlena-Odeh method 143-144
History match 142 156 205-206
History (reservoir management) 6-7
Horizontal well 208 210 240-244Horizontal well results 208 210
Horner analysis 96 99 104-107
109 111
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Hydrocarbon reservoir 116-117
Hyperbolic decline curve 131 133 135
I
Imaging 60 63
Implementation 18 192-193
reservoir management 18
field development 192-193
Importing files 66
Incompatibility problem 25
Induction-electric log 41
Information technology xix
Injection data 23
Injection test 95
Input data gathering 156
Integrated model 16 30-35
Integrated software package 155 162-164
Integrated reservoir management xix 3
Integration of professionals 5-11 30-35
teamwork 8-9
Integration (data/software) 202
Interference 93-95 106
test 93-95 106
Internal rate of return 228
Internet 165-166 168
Interpolation 74
Investment efficiency2
27-228
Isopach contour 123-126
Isopach map 31 123-124
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K
Kappa Engineering 169
Keying in 66
Kriged map 83-84 89
Kriged value 81
Kriging 80-81 84-87
L
Landmark Graphics 172
Laterolog 41
Layer components 68
Lithology log 39-40
spontaneous potential 39-40
gamma ray 40
Log analysis 43-51
software 43-51
example analysis 43-51
data entry 44-45
data preparation 44 46
data analysis 44 47-48
cross sections 46 49
summations 46-47 50-51
Log analysis procedure 43
Log interpretation 38
Log-inject-log 26
Log-log plot 99 102 104-105
107 111
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M
Management process 11-18
Map editing 66
Map statistics 76-83
Map validation 24
Mapping (conventional) 75-77
Mapping example 66-69
display items control 67-69
Mapping/daTa visualization 65-71
control data 66
data entry 66
software 66-69
mapping example 66-69
model data 68-69
alternative displays 69-71
Material balance 27 32 117
119 141-150 205
Material balance method 141-150
oil reservoirs 142-143
gas reservoirs 143-144
software 144-149
example analysis 144-149
case study 144-149
Material balance study (oil reservoir) 144-149
Mathematical model 82 117-119
Mature field study 195-211
North Apoi/Funiwa field example 195-211
field description 195-200
objectives 199
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Mature field study (Cont .)
challenges 199
approach 201
deliverables 200-205
software 202-203 205-210
decline curve analysis 203
classical material balance 205
reservoir simulation 205-210
contributions by partners 209-210
results 211
MBAL 145 147-149
Measurement needs (well log) 37-39
Measuring uncertainty 85-87 89-90
Merak 172-173
MERLIN 234-253
Mesh plot 69-70
Migration (depth/time) 58-61
Miller, Dyes and Hutchinson analysis 96
Mini-simulation 233-253
examples 233-253
software 233-253
single vertical well 237-240
single-lateral horizontal well 240-244
single well water coning 245-249
five-spot waterflooding 249-253
Model data 68-69
Model diagnostic 108-109
Model misuse 157-159
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Model/modeling 1-2 4 29-36
217 219 221
223-224
Monitoring/surveillance 18 193
Multidisciplinary study 6-11 30-35 202
Multi-Fluid 170
N
Net pay 123-124
Net thickness 75-80 85-86 89-90
215-218
Neutron log 42
New field development plan 181-193
Nodal analysis software 206
North Apoi/Funiwa field example 195-211
Nugget 78 80-81
Numerical analysis 151-152
Numerical simulator 17
O
Objectives (field study) 199
Office of Fossil Energy 178
Oil reserve 200 205 207-209
221-223
Oil reservoir 115-117 142-143
Oil reservoir material balance study 144-149
Oil saturation 26 209
OILWAT 205
Organization/teamwork 9-11
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Organizational structure 6-11
Original gas in place 126-127
Original oil in place 126-127 142-144 205
207-209
P
P/Z method 144
Partnership study 209-210
Payout time 227
Performance evaluation 17-18 27
Performance prediction 156
Permeability 31 38 215-217
219
Petroleum Experts 145
Petroleum Technology Transfer Council 179
Petroleum WorkBench 43-51 66-71 109-113
168 202-203 210
Petrophysics 33
Petrotechnid Open Software Corporation 25 164-165 176-177
PetroWorks 172
Phase behavior 115-116
Phase/direction of flow 152-153
Plan development 14-18
Planimetering 122 124
Planning process 14-18
Porosity 31 38 51
75 88 215-218 Porosity display 70
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Porosity log 41-43
acoustic velocity (sonic) 41-42
density 42
neutron 42
combination 42-43
Porosity map 74
Post-stack time migration 60-61
Present worth index 227-228
Present worth net profit 227
Pressure behavior (reservoir) 99 101-106
Pressure buildup 26 94-95 98-100
Pressure buildup/falloff test 26
Pressure data 103
Pressure drawdown 94-95 97-98
Pressure falloff 26 94-95
Pressure history 111
Pressure matching 156-157
Pressure method 143-144
Pressure transient testing 93-113
Pressure-volume-temperature data 24 31
Pre-stack depth migration 60-61
Probability density function 85-86 91
Probability map 91
Probability plot 90
Producing mechanisms 32 116 118
Producing zone depth 38
Production data 23 129
Production Data Analysis 136-138
Production decline curve 129-140
Production forecast 16 187-191
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Production history 31
Production log 26
Production performance analysis 115-120
oil and gas reservoirs 115-117
natural producing mechanisms 116 118
analytical estimates 117-119
reserves 118-120
Production phase 7
Production rate 13 31 130-131
187-191 221-226
forecast 187-191
Production rate plots 130-131
Production/engineering model 16
Productivity matching 156 159
Profit to investment ratio 227
Profitability 13-14
Profitability index 227-228
Project development 213-232
Proprietary software 162
PVT behavior 153
PVT properties 24 31 23S-236
Q
Questions (well test) 106 108
R
Radial model 237-240 245-249
Range 78 81
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Realization 82-83 88 91
comparison 88
Recovery efficiency 119-120 127
Recovery factor 119-120 199 223
225-226
Recovery scheme 184
Reflection strength 61
Relative permeability 31 222 236-237
Repeat formation test 26
Reserve (definition) 118-120
Reserve estimate 16 27 117-120
142 184
Reserves 16 27 117-120
142 184 187-191
221 223-226
estimate 16 27 117-120
142 184
forecast 187-191
Reservoir boundary 93 106
Reservoir characteristic 185-186
Reservoir Characterization Research and Consulting 170
Reservoir data 22-23 103 182-183
185-186 215-225
Reservoir heterogeneity 32
Reservoir knowledge 15 21-27
Reservoir life process 7
Reservoir management concepts/practices 5-19
definition 6
history 6-7
reservoir life process 7
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Reservoir management concepts/practices (Cont .)
integration/teamwork 8-9
organization/teamwork 9-11
management process 11-18
Reservoir management process 11-18
goal setting 13-14
plan development 14-18
implementation 18
surveillance/monitoring 18
evaluation 18
Reservoir model/modeling 1-2 4 16
29-36 186-187 203
217 219 221
223-224
geoscience role 30-32
engineering role 31-32
integration of data 32-35
Reservoir performance 1-2 4 16-17
24 27 29
32-34 115-120 129-150
Reservoir pressure 31 93
Reservoir property 75 93
Reservoir simulation 151-160 205-210 234
concept and well management 151-152
spatial/time discretization 152
phase/direction of flow 152-153
fluid flow equations 153-154
reservoir simulators 154-155
simulation process 155-159
abuse of simulation 156-157
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Reservoir simulation (Cont .)
misuse of models 157-159
software 160
Reservoir simulator 17 151-160
Reservoir study 181-193 213-232
Reservoir temperature 31 91
Reservoir volume 117 119 122-127
free gas/gas cap 126
oil in place 126-127
solution gas 127
Residual oil 126
Resistivity log 37 40-41
electric 41
induction-electric 41
dual induction 41
guard log (laterolog) 41
Review 206
Rock type 38
Rock property 23 106
S
SAPHIR 169
Saturation matching 156 158
Schlumberger 176
Screen editing 66
Seed point 63
Seismic cross section 59Seismic data 23 55-58 60
70-71 198
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Seismic data analysis 53-64
seismic measurements 54-58
seismic data processing 55-58
structural interpretation 58-61
stratigraphic interpretation 61-62
3-D visualization 62-64
software 62-64
Seismic data interpretation 60 70-71
Seismic data processing 55-58
Seismic measurements 54-58
Seismic record 54 58
section 58
Seismic reflection 55-58 61-62 88-89
amplitude 55-56 61-62 88-89
Seismic survey 30-31
Seismic trace 55
SeisWorks 172
Semi-log analysis 109
Sensitivity analysis 188 231-232
Sill 78 81
Simpson’s rule 125-126
Simulation 4 17 27
32 81-84 88
91 109 117-119
151-160 205-210 232-253
191 205
map 82-83
reservoir 91 109 117-119
151-160 205-210
models 153
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Simulation (Cont .)
process 155-159
abuse 156-157
examples 232-253
Simulation abuse 156-157
Simulation examples 232-253
Simulation map 82-83
Simulation models 153
Simulation process 155-159
Simulation (reservoir) 91 109 117-119
151-160 205-210
Simulators 17 151-160
Single vertical well 237-240
Single-lateral horizontal well 240-244
Skin effect (well) 101
Smedvig Technologies 170
Soft data 74 88-89
Software xix-xx 104 35
43-51 62-64 66-69
102-107 109-113 135-140
144-149 160-179 202-203
205-210 217 219
221 223-224 233-253
well log analysis 43-51
seismic data analysis 62-64
3-D visualization 63
mapping 66-69
data visualization 66-69
type-curve analysis 102-107
well test analysis 102-107 109-113
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Software (Cont .)
well test design 112-113
decline curve method 136-140
material balance 144-149 205
reservoir simulation 160
sources of 164-165 168-179
field study 202-203 205-210
waterflood 217 219 221
223-224
simulation 233-253
Solution gas 127 142
Solution gas drive 142
Sonic log 41-42
Sources (software) 164-165 168-179
Spatial correlation 78
Spatial/time discretization 152
SPE Gulf Coast Section 174
SPE International 174-175
Specification (computing) 25
Spontaneous potential log 37 39-40
Stacked traces 59
Stacking (seismic data) 58-61
Stand-alone software 155 162-163
Standard method (decline curve) 136-138
Standard/standardization 164-165
Statistical characteristics 32
Statistical distribution of data 73-74
Statistical trend 80
Stochastic simulation 32
Storing/retrievd (data) 24-25
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StrataModel 172
Stratigraphic cross-section 198
Stratigraphic interpretation 61-62
Structural contours 68-69
Structural interpretation 58-61
Structure control 67
Structure display 70
Structure map 31 59 122-123
182-183 214
Structure-dependent properties 69
Summations 46-47 50-51
Support structure 6
Surface components 67-68
Surveillancel/monitoring 18 193
Synergy 6-7
T
Teamwork 8-11
Technology transfer 179 202 209-210
Technology trends xix
Test types 94-95
Texaco EPTD 165-167 197 199
205 209
Texaco Overseas (Nigeria) (TOPCON) 195 197 199
208-211
Thermal simulator 154
Time-lapse log 26Tracer test 26
Transient fluid flow theory 95-96
Transparent data 71
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Trapezoidal rule 125
Trend study 74 76-77
Type curve analysis 99 101-107 109-112
software 102-107
example analysis 102-107
Type-curve match 102 204 109
U
U.S. Department of Energy 172-173 176-178
Ultimate recovery 119-120 199
Uncertainty 74 81 85-87
89-91
V
Validation (data) 24
Value of asset 6
Variance 74 77-78 88
Variogram 74 77-83 88
terminology 81
Variogram ellipse 79-80 83
Velocity model 60-61
Vertical resolution 57
Vertical well 237-240
Visualization (data) 62-71
3-D 62-64
example 66-69
Visualization (3-D) 62-64
Volumetric method (reserve estimate) 27 117 119
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W
Water coning 245-249
Water cut plots 130-132
Water drive 143
Waterflood design cases 221 223 225-226
229
Waterflood model 249-253
Waterflood project development 213-232
example analysis 213-232
reservoir data 215-222
software 217 219 221
223-224
reservoir modeling 217 219 221
223-224
production rates 221 223-226
reserves 221 223-226
economic evaluation 225 227-232
Waterflooding 191 249-253
Wavelet 55
Well log analysis 37-51 75
measurement needs 37-49
log types 39-43
example analysis 43-51
Well log data 25-26 75 195
199
Well log types 39-43
caliper 39
lithology 39-40
resistivity 40-41
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Well log types (Cont .)
porosity 41-43
log analysis procedure 43
example analysis 43-51
Well management 151-152
Well model 237-244
Well performance 237-253
Well property 108
Well spacing 184 187-191
Well test analysis 30 93-113
test types 94-95
transient fluid flow theory 95-96
conventional analysis 96-99
drawdown 97-98
buildup 98-100
type-curve analysis 99 101-106
software 102-107 109-113
example analysis 102-107
questions 106 108
checks during analysis 108-109
Well test data 26
Well test design 109 112-113
Well test interpretation 102-106
Well testing 23
Well values 66
Wellbore condition 93
Wellbore damage 99
Wellbore storage 101
Wireline logging devices 37
Wizard field example 181-193
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