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QUANTIFYING INCREMENTAL OIL PRODUCTION AND ECONOMICS OF USING
INTELLIGENT COMPLETION AS A TOOL FOR RESERVOIR MANAGEMENT
BY
BENTIL NANA ESI BENYIWA
A THESIS
SUBMITTED TO THE AFRICAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
ABUJA - NIGERIA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER
OF SCIENCE IN PETROLEUM ENGINEERING
SUPERVISOR: PROF. DAVID O. OGBE
MAY 2013
ii
QUANTIFYING INCREMENTAL OIL PRODUCTION AND ECONOMICS OF USING
INTELLIGENT COMPLETION AS A TOOL FOR RESERVOIR MANAGEMENT
By
Bentil Nana Esi Benyiwa
RECOMMENDED:
Prof. David O. Ogbe – Committee Chair
Prof. Wumi Iledare – Committee Member
Dr. Alpheus Igbokoyi – Committee Member
APPROVED:
Prof. Godwin Chukwu
Chair, Department of Petroleum Engineering
Prof. Wole Soboyejo
Provost, African University of Science and Technology
Date
iii
DEDICATION
To my Mum and Dad, Madame Grace Abassah and Mr. Joseph Bentil
iv
ACKNOWLEDGEMENT
Firstly, I am grateful to the Almighty God for the blessings throughout my program at the
African University of Science and Technology (AUST).
I acknowledge and appreciate the effort of my supervisor, Prof. David O. Ogbe for his assistance
and guidance throughout this thesis. My gratitude also goes to my thesis committee members,
Prof. Wumi Iledare and Prof. Alpheus Igbokoyi for their contributions to this work.
I am grateful to my mum, Madame Grace Abassah and my Dad, Mr Joseph Bentil for their
support throughout my program.
Finally, I would acknowledge all the distinguished lecturers that I encountered during my
program and friends who contributed to the success of this work.
v
ABSTRACT
Huge amount of hydrocarbon in place is left unrecovered. Integrated reservoir management, in
addition to the use of new technologies improves hydrocarbon recovery. Intelligent completion is
one of the technologies which enhance reservoir management thereby improving the
hydrocarbon recovery.
This work presents a review of intelligent completion technology, guidelines to evaluate the
decision whether or not to implement intelligent completion and evaluates field cases of
intelligent completion installation.
The case studies were derived from four fields where intelligent completions have been
implemented. Comparison of intelligent completion with non-intelligent completion was based
on ease of data acquisition for reservoir management, incremental oil production and
profitability criteria. The yard sticks used for economic analysis include the net present value,
discounted payout period, profitability index and growth rate of return.
The results from the study show that reliable intelligent completion improves reservoir
management by enabling data acquisition and well monitoring. Employing intelligent well
completions in reservoir management can lead to 21% to 38% increase in oil recovery and 17%
to 41% increase in NPV compared to non-intelligent completion. It must be pointed out that
intelligent system failure may render intelligent completion projects economically unattractive.
The results of this study can be used to evaluate the feasibility of executing an intelligent
completion project; especially in fields were intelligent completion is yet to be implemented as a
tool for reservoir management.
vi
TABLE OF CONTENT
DEDICATION ............................................................................................................................... iii
ACKNOWLEDGEMENT ............................................................................................................. iv
ABSTRACT .................................................................................................................................... v
TABLE OF CONTENT ................................................................................................................. vi
LIST OF FIGURES ....................................................................................................................... ix
LIST OF TABLES ......................................................................................................................... xi
CHAPTER 1 ................................................................................................................................... 1
INTRODUCTION .......................................................................................................................... 1
1.1 INTRODUCTION ................................................................................................................. 1
1.2 STATEMENT OF THE PROBLEM .................................................................................... 3
1.3 OBJECTIVES OF THE STUDY .......................................................................................... 4
1.4 SCOPE OF STUDY .............................................................................................................. 4
1.5 ORGANIZATION OF THESIS ............................................................................................ 5
CHAPTER 2 ................................................................................................................................... 6
2.1 LITERATURE REVIEW ...................................................................................................... 6
2.2 RESERVOIR MANAGEMENT ......................................................................................... 11
2.3 INTELLIGENT WELL COMPLETION ............................................................................ 14
2.4 CLASSIFICATION OF MONITORING SYSTEMS ........................................................ 17
2.5 COMPONENTS OF IWC ................................................................................................... 18
2.6 APPLICATIONS AND BENEFITS OF IWC .................................................................... 19
vii
CHAPTER 3 ................................................................................................................................. 24
METHODOLOGY ....................................................................................................................... 24
3.1 INTRODUCTION ............................................................................................................... 24
3.2 RESERVOIR DATA ACQUISITION AND MANAGEMENT ........................................ 26
3.3 WELL PERFORMANCE ................................................................................................... 27
3.4 TIME VALUE OF MONEY AND ECONOMIC ANALYSIS .......................................... 32
CHAPTER 4 ................................................................................................................................. 37
RESULTS AND DISCUSSION OF CASE STUDIES IN RESERVOIR MANAGEMENT........ 37
4.0 INTRODUCTION .............................................................................................................. 37
4.1 ASSUMPTIONS COMMON TO ALL FOUR CASE STUDIES ..................................... 37
4.2 CASE STUDY 1: COMMINGLED PRODUCTION FROM A TWO-LAYER
OFFSHORE FIELD ................................................................................................................. 38
4.3 CASE STUDY 2: MULTI-LATERAL PRODUCER-INJECTOR PATTERN OFFSHORE
FIELD ....................................................................................................................................... 45
4.4 CASE STUDY 3: TRIPLE COMINGLED PRODUCTION – USARI FIELD OFFSHORE
NIGERIA .................................................................................................................................. 52
4.5 CASE STUDY 4: HORIZONTAL WELL PRODUCTION – OSEBERG FIELD
OFFSHORE NORWAY ........................................................................................................... 59
CHAPTER 5 ................................................................................................................................. 67
CONCLUSIONS AND RECOMMENDATIONS ........................................................................ 67
5.1 SUMMARY AND CONCLUSIONS.................................................................................. 67
5.2 RECOMMENDATIONS .................................................................................................... 68
viii
NOMENCLATURE ..................................................................................................................... 69
REFERENCES ............................................................................................................................. 71
APPENDIX ................................................................................................................................... 76
ix
LIST OF FIGURES
Figure 2.1: Reservoir management (Adapted from Satter et al., 1994) ........................................ 12
Figure 2.2: Reservoir management process (Fowler et al., 1996) ................................................ 13
Figure 2.3: Schematics of IWC (adopted from Sakowski, 2005) ................................................. 15
Figure 2.4: Intelligent wells installation trend from all providers (Eni Group, 2006) .................. 16
Figure 3.1: Workflow used for reservoir and economic analysis of IWC .................................... 25
Figure 3.2: Detailed Step-by-Step Procedure for Reservoir and Economic analysis of IWC
application (Adapted from Sakowski, Anderson and Furui, 2005) .............................................. 26
Figure 4.1: Production performance from the IWC and the conventional well............................ 40
Figure 4.2: NPV sensitivity analysis for both IWC and conventional well .................................. 42
Figure 4.3: Tornado charts of the NPV for the IWC and the conventional well .......................... 43
Figure 4.4: Cumulative discounted net cash flow versus time ..................................................... 43
Figure 4.5: Oil production history from both completions (Ajayi et. al, 2006) ............................ 46
Figure 4.6: Oil production performance for the two scenarios ..................................................... 48
Figure 4.7: NPV sensitivity analysis for both completions .......................................................... 50
Figure 4.8: Tornado chart of the NPV for both wells ................................................................... 50
Figure 4.9: Cumulative discounted net cash flow versus time ..................................................... 51
Figure 4.10: Development well path (Brock et al., 2006) ............................................................ 53
Figure 4.11: Production history from the IWC for Case Study 3 (Brock et al., 2006) ................. 53
x
Figure 4.12: Oil production forecast for Case Study 3 ................................................................. 55
Figure 4.13: Spider and Tornado charts of the NPV .................................................................... 56
Figure 4.14: Sensitivity analysis of the NPV ................................................................................ 57
Figure 4.15: Cumulative discounted net cash flow versus time ................................................... 57
Figure 4.16: Production performance in both wells...................................................................... 62
Figure 4.17: Tornado charts of the NPV for both wells ............................................................... 64
Figure 4.18: Sensitivity on NPV for both completions................................................................. 64
Figure 4.19: Cumulative discounted net cash flow versus time for both wells ............................ 65
Figure B1: NPV certainty analysis for IWC ................................................................................. 78
Figure B2: NPV uncertainty analysis for Conventional Completion ........................................... 79
Figure B3: Spider Chart for both completions .............................................................................. 80
Figure C1: NPV uncertainty analysis for IWC ............................................................................. 80
Figure C2: NPV uncertainty analysis for NON -IWC .................................................................. 81
Figure D1: NPV uncertainty analysis for IWC ............................................................................. 82
Figure E1: NPV uncertainty analysis for IWC ............................................................................. 83
Figure E2: NPV uncertainty analysis for NON -IWC .................................................................. 83
Figure E3: Spider Chart for both completions .............................................................................. 84
xi
LIST OF TABLES
Table 3.1: Some correlations used in the industry ........................................................................ 30
Table 3.2: Arp’s equations ............................................................................................................ 30
Table 4.1: Variable distribution input ........................................................................................... 38
Table 4.2: Stochastic variable input parameter distribution for Case Study 1 .............................. 39
Table 4.3: Summary of results for Case Study 1 .......................................................................... 44
Table 4.4: Stochastic variable distribution of input parameters for Case Study 2 ........................ 47
Table 4.5: Summary of results for Case Study 2 .......................................................................... 51
Table 4.6: Summary of results obtained for Case Study 3 ........................................................... 58
Table 4.7: Stochastic variable distribution of input parameters of Case Study 4 ......................... 60
Table 4.8: Summary of the results for Well B-21B ...................................................................... 65
Table A1: NPV calculation for IWC ............................................................................................ 76
Table A2: NPV calculation for Conventional Completion ........................................................... 77
Page 1
CHAPTER 1
INTRODUCTION
1.1 INTRODUCTION
Reserves are the main asset of the exploration and production (E&P) industry. Every E&P firm
aims at maximizing its total profit in the long run, hence the industry aims at enhancing ultimate
recovery of a field, cost efficiently. However, most of the hydrocarbons in place are not
recovered; about 35% of hydrocarbons in place are recovered leaving behind the remaining 65%.
The need to improve recovery from the huge amount of remaining hydrocarbons in place around
the world requires sound reservoir management practices.
Integrated reservoir management is a continuous process and the key to successful operation of
the reservoir throughout its entire life. It requires the use of both multi-disciplines and
technological resources for maximizing profit. A comprehensive reservoir management plan
involves depletion and development strategies, data acquisition and analyses, geological and
numerical model studies, production and reserves forecasts, knowledge of facilities requirement
and economic optimization. These can facilitate better reservoir management which will enhance
economic recovery of hydrocarbons (Satter et al., 1994). Intelligent well completion forms part
of the overall vision of reservoir management optimization.
An intelligent well completion (IWC) is completion system capable of measuring, transmitting
and analyzing wellbore production, reservoir and completion integrity data, and enabling remote
action; change valve chokes and optimize these parameters to better control reservoir, well and
production processes (Eni, 2006 ). The concept of intelligent completion does not generally refer
Page 2
to any capability for automated self-control but rather manual interface to initiate instructions to
the well (Robinson, 2007).
Reservoir parameters are continuously monitored for each zones with permanent pressure and
temperature gauges, base on which the valve chokes are reconfigured to allow simultaneous
production from more zones through a single string or well. Remote completion monitoring is
the ability of a system to provide data, obtained in or near the wellbore, without requiring access
and entry for conventional intervention to the well. Hence IWC technology provides great
flexibility in the operation of conventional wells and multilateral wells; as each branch of the
well can be controlled independently. (Yeten et al., 2004)
The basic element of IWC are acquisition and transmission system, flow control valves (FCV)
and actuation system. The acquisition and transmission system is a set of equipment used to
transmit and acquire reservoir data, while the FCVs control flow rate from a zone or in a level.
And the actuation system is a set of equipment that supplies power to the valves (Eni, 2006)
Compared to conventional completions, IWC offers great benefits. The primary objectives of
IWC are normally to maximize or optimize and anticipate oil recovery, control gas and water
breakthrough, reduce cost and improve safety. Zero intervention especially subsea or remote
location wells and production optimization for multi zones reservoir (simultaneous production),
horizontal wells, complex reservoir structure, auto gas lift, etc. justifies the installation of
intelligent completions (Eni, 2006).
Full benefits of IWCs are reservoir specific; it depends on reservoir quality, production rates,
fluid contacts, gas and water coning. The control lines, cables and sensors represent the nervous
Page 3
and circulatory system of an IWC and damage to these elements may result in partial or total loss
of its function. There could be the risk of system failure, for instance with sand control, there
could be erosion of choke elements, seal surfaces, control lines and interference with device
movement. High reliability of intelligent completion is necessary for avoiding workover
operations whereas monitoring and measuring of downhole reservoir data and choking ability are
important to reach production optimization throughout the entire life of a reservoir. (Robinson,
2007)
This work compares conventional completions with intelligent well completions by analyzing
case studies of some reservoirs where intelligent completions have been installed.
1.2 STATEMENT OF THE PROBLEM
This study is designed to answer the following questions:
For the reservoir under consideration,
What extent does IWC improve data acquisition in reservoir management?
What extent does IWC improve reservoir performance, in terms of oil production/,
recovery, water and gas production control?
Is the use of IWC economically viable?
Page 4
1.3 OBJECTIVES OF THE STUDY
The objectives of this study are:
To quantify the incremental oil production from the application of IWC as a reservoir
management tool
To ascertain the economic viability of IWC compared to non-intelligent completion
To guide reservoir management team in the decision to use intelligent completion or not
1.4 SCOPE OF STUDY
The case studies presented in this work described the analyisis of four (4) reservoirs where IWC
has been employed. The analysis considered reservoir performance - production and recovery,
water/gas production control and economics or cost implication – OPEX, CAPEX and
profitability indicators such as Net Present Value (NPV), Payout period (PO), Growth Rate of
Return (GRR) and Profitability Index (PI).
Monte-Carlo simulation and sensitivity analysis are carried out using Crystal Ball (Oracle, 2012)
to account for the project risks in the economic analysis.
Page 5
1.5 ORGANIZATION OF THESIS
This thesis is divided into five chapters. Chapter 1 introduces the problem of this study, the
objectives and the scope of the study. Chapter 2 discusses literature review of related studies
previously done on this problem and reviews the concept of reservoir management and
intelligent well systems. Chapter 3 discusses the methodology, which describes how the selected
reservoirs are analyzed for the justification of the use of intelligent well completion. Chapter 4
focuses on the results and discussion of the results of the study. And finally, the conclusion and
recommendations are covered in Chapter 5.
Page 6
CHAPTER 2
2.1 LITERATURE REVIEW
Since 1997, when the first intelligent well (IW) was installed in Saga Snorre TLP North Sea-
Norway, intelligent well technology has been used in many kinds of production wells all over the
world, including off-shore wells, vertical conventional wells, horizontal wells and multilateral
wells. Before 1997, all wells were completed with a common completion including hydraulically
sliding sleeves and tubing. The evolution of downhole gauges, sliding sleeves and surface
controlled subsurface safety valves resulted in the development of intelligent wells. (Dekui et al.,
2012)
From 1997, several studies have been published to demonstrate the importance of the application
and benefits of intelligent well completion (IWC), especially for multiple reservoirs where
commingled production is the main production strategy (Jalali et al., 1998; Lucas et al., 2001).
The application and potential benefits of IWC for production from a single reservoir have been
demonstrated in several studies (Yu et al., 2000; Yeten and Jalali, 2001; Jansen et al., 2002;
Valvatne et al., 2003).
Sharma (2002) presented a method to apply real options theory to quantify the value of
intelligent well applications, including the value of reducing project volatility and risk. He
described how mathematical model can be incorporated into a larger workflow process to assess
entire asset portfolios which can be used as a tool for screening reservoir assets for potential
IWC applications and also help optimize the design of the completion.
Yeten et al. (2004) determined the optimal performance of IWs using gradient based
optimization technique in conjunction with a reservoir simulator. They considered the effect of
Page 7
uncertainty in reservoir description and equipment reliability and noted that downhole control
can compensate to some extent for geological uncertainty, even when there is the possibility of
equipment failure. They also noted that, the impact of equipment reliability was related to both
the timing and type of failure; generally the earlier the valves failed the larger the negative
impact.
Vachon and Furui (2005) illustrated how IWC can enhance the electrical submersible pump
(ESP) performance and add flexibility by using downhole chokes to optimize ESP performance.
Their study focused on single ESP wells producing from multiple pay zones. It was established
that the intelligent completion systems with remotely controlled chokes allow for optimal
production rates, maintain the optimum ESP operating range and reduce risk of pump failure.
Thus, IWC eliminates the expense of intervention and the associated loss in production, due to
extended ESP life, reduction of cost of replacing damaged pumps and pump down time.
Sakowski (2005) looked at the impact of intelligent well completions on total economics of field
development. Reservoir performance analysis and economic evaluation tools were used to
quantify the value of IWC. IWC projects performed better in relatively cost sensitive
environments since they can maintain oil production while reducing the capital and operating
costs. He noted that the ability to respond to expected changes in reservoir performance is also a
valued benefit and the technology has advanced rapidly from more high-cost, offshore
application environment to more revenue-sensitive operating environment due to the ability to
clearly demonstrate economic value of IWC over alternate conventional completions.
Aggrey et al. (2006) employed a synthetic reservoir to explore and compare the value of
extensive, accurate measurements with a higher chance of system failure with the deployment of
Page 8
lower resolution sensors of greater reliability. A methodology to calculate the value of
information and expected opportunity loss parameters for IWC of different capabilities was
developed. It was shown that value creation from IWC and real time optimization is strongly
dependent on the ability of the system to function properly throughout the equipment’s specified
lifetime.
Aggrey and Davies (2007) presented an enabler for IWC decision making process where
stochastic coupling of the reliability profile and reservoir performance is employed. The
proposed workflow allows the inclusion of conventional stochastic analysis for economic and
geologic risks. The evaluated scenarios showed increased value potential for IWC
implementation.
Addiego-Guevara et al. (2008) investigated whether simple reactive control strategies based on a
feedback loop between inflow control valve (ICV) settings and surface or downhole
measurements can enhance production and mitigate reservoir uncertainty if they are designed to
work across a range of production scenarios. The implementation of an intelligent horizontal
well in a thin oil rim reservoir in the presence of reservoir uncertainty was assessed. They
evaluated the benefit of using two completions in conjunction with surface and downhole
monitoring. It was found that reactive control strategy can insure against reservoir uncertainty.
However, a simple reactive control strategy using variable ICVs adjusted in response to
downhole measurements of phase flow rates yielded a neutral or positive return regardless of
reservoir behavior. They suggested that downhole reservoir imaging techniques which can
monitor fluid flow and saturation changes at a distance from the well may be used in a proactive
feedback loop.
Page 9
Pari et al. (2009) presented a comprehensive review of state-of-the-art intelligent well
technology considering the benefits, types of sensors, challenges, economics and application in
fractured reservoir. They concluded that IWC aids reservoir management although there could be
the risk of system failure. To mitigate the risk of system failure, the use of cable-less power and
communication system in IWC was recommended.
Grebenkin and Davies (2010) conducted a study on the impact of geological uncertainty and
uncertainty in the dynamic parameters such as fluid contacts, relative permeability, aquifer
strength and zonal skin on the flow control ability of an IW to reduce the production uncertainty.
The results emphasized the importance of the probabilistic approach for production prediction
and illustrated its use as a tool to justify the installation of IW technology in a particular well. It
was found that the uncertainty of the dynamic parameters had a higher impact on the total oil
production than uncertainty associated with reservoir geology.
Rodriguez and Figueroa (2010) evaluated the applicability of multi-purpose intelligent
completions for high productive oil reservoir from both the productivity and the operational
standpoints. They noted that IWC in naturally fractured mature fields tends to increase oil
production and reduce the field decline. As water production increases, the producing zones can
be shut off as needed to reduce the overall production of water. Hence IWC will improve the life
cycle value of mature field.
Hudson et al. (2011) reviewed case studies of about thirty (30) oil and gas production fields
containing IWs to consider the work process that was used to justify the incremental investment
in hardware and installation cost. The study outlined key findings from the review,
recommended a project-stage-base modeling workflow and presented opportunities for
Page 10
improvements to support more rigorous and efficient design decisions. They noted that IWC
justification is typically attributed to reduction of lifecycle costs, accessing marginal reserves and
certain reservoir management concerns and suggested that IWC modeling workflow requires
multi-disciplinary collaboration and sometimes require simulation experts to handle reservoir
uncertainties.
Dekui et al. (2012) considered some advanced downhole devices and the prospects for IW
application in the Daqing oil field. It was concluded that IWs improves oil recovery and reservoir
management. IW technology was effective in Daqing oil field, average water cut decreased from
89% to 70% and average oil production increased by 40%.
Gulyaev et al. (2012) noted that IW is an important part of production technology from low and
extremely low permeable reservoirs. IW equipment with remote downhole control significantly
increased well construction cost but with tax reduction the oil production from such reservoirs
become economical.
Griffith et al. (2012) evaluated IW system at the Saramacca oil fields. Well performance was
monitored monthly based on the volume flow test and flowing bottomhole pressure (BHP)
measurements. Downhole pressure data was also collected for build up tests and other reservoir
studies. It was concluded that IWC offers several advantages over the conventional well.
Barreto et al. (2012) presented a methodology to optimize production using water cut as a
parameter to shut down wells and IWC as a variable of the optimization process and as an
economic indicator to evaluate well completion efficiency. The results show the importance of a
Page 11
good estimation of the time to shut down wells and completions to reach the optimum production
potential of the reservoir.
2.2 RESERVOIR MANAGEMENT
The need to enhance recovery from the huge amount of remaining hydrocarbon around the world
requires a sound reservoir management practice which is a continuous process throughout the
entire life of a reservoir.
In the past, non-integrated reservoir management was practiced only when a major expenditure is
planned, but during the last 20 years emphasis has been put on integrated reservoir management
where there is full coordination of geologists, geophysicists and petroleum engineers to advance
petroleum exploration, development and production (Satter et al., 1994).
There are many papers with different definitions of reservoir management. Satter, describe
reservoir management as the use of available human, technological and financial resources to
maximize profits from a reservoir by optimizing recovery while minimizing capital investments
and operating expenses. Reservoir management is carried out purposefully to control operations
in order to obtain the maximum possible economic recovery from a reservoir on the basis of
facts, information and knowledge (Thakur, 1996). Technological advances and computer power
provide tools for better reservoir management. A team approach based on integration of
geosciences and engineering personnel, tools, technology and data are essential for sound
reservoir management practice (Satter et al., 1994). Figure 2.1 illustrates schematics of reservoir
management.
Page 12
Figure 2.1: Reservoir management (Adapted from Satter et al., 1994)
Reservoir management does not refer to a high-tech approach to improving production in large
reservoirs and it is not an optional activity because every reservoir has to be managed. Reservoir
management practices involve goal setting, planning, implementing, monitoring, evaluating and
revising initial plans throughout the entire life of a reservoir from exploration to abandonment
(Fowler et al., 1996). Figure 2.2 presents a schematic illustration of reservoir management
process.
TECHNOLOGY
DATA
TOOLS
PEOPLE
Page 13
Figure 2.2: Reservoir management process (Fowler et al., 1996)
No
Yes
No
Yes
Monitor Reservoir
Management Plan Predictions
Monitor Business and
Technology Environment
End Points or
Anomalies
Encountered?
New
Developments
Applicable?
Implement Reservoir
Management Plan
Reservoir
knowledge
Business
Environment
Knowledge
Technologies
Knowledge
Construct (or Revise) Reservoir Management Plan
of Appropriate Scale and Scope
Page 14
Intelligent well completion (IWC) forms part of the overall vision of reservoir management and
automation system. Reservoir management using intelligent completions enables better
understanding of the reservoir, resulting in improved reserves recovery. For a complex reservoir,
completion design in terms of monitoring and control are better handled with intelligent
completions compared to conventional completions. Intelligent wells implementation in a major
project requires the joint effort of many disciplines, an integrated workflow that systematically
integrates the contributions from various disciplines throughout the lifecycle of the project which
is the key to success (Lau, 2008).
2.3 INTELLIGENT WELL COMPLETION
Intelligent wells completion (IWC) can provide reservoir control through continuous monitoring
and valve actuation in real-time, capable for transmitting, collection and analyzing wellbore,
production, reservoir and completion integrity data allowing remote control of reservoir, well
and production processes without requiring access and entry for conventional intervention to the
well (Robinson,2007). Downhole sensors and control devices are combined with a surface or
subsurface unit for production optimization; the systems are programmed to optimize a given
parameter such as net production by varying for example the inflow profile from various zones.
(Robinson, 2007)
IWC consists of packers, hydraulic and/or electrical control lines, inflow control valves, and a
surface control unit. The packers isolate the individual zones along the well path. The inflow
control valve (ICV) enables choking or shutting different zones according to performance like
drawdown, water cut, gas-oil-ratio, etc. The control lines are used for power transmission to the
ICV and transfer of monitored downhole data like pressure and temperature. The surface control
Page 15
unit is used for handling all the monitored data and for remote operation of the downhole inflow
control valves. There are IWC with infinitely variable chokes and extensive monitoring like
pressure, temperature, sand detectors, multi-phase metering, and resistivity and seismic sensors
for tracking near well fluid contacts (Erlandsen, 2000). Figure 2.3 illustrates schematic of IWC.
Figure 2.3: Schematics of IWC (adopted from Sakowski, 2005)
Robinson (2007) reported that until the late 1980s, remote monitoring was generally limited to
surface pressure transducers around the tree and surface choke, with remote completion control
Page 16
restricted to the hydraulic control of safety valves and (electro-) hydraulic control of tree valves.
Data are now transmitted to remote offices and interpreted from the well site. Recent and
developing remote monitoring and control capabilities include: multiphase flow measurement;
chemical composition and sand detection; multiple sensors and flow monitoring; remote-control
gas-lift valves, flow-control sleeves, valves, and packers; along-hole profile detectors for
pressure and distributed temperature; and seismic geophones and resistivity sensors. There is an
increasingly adoption of IWC due to the identifiable practical and economical advantages of its
application. Installation of IWC systems multiplied from 2000 to 2002, running at 50 well
systems per year (Robinson, 2007). Figure 2.4 shows IWC installation increasing trend globally.
Figure 2.4: Intelligent wells installation trend from all providers (Eni Group, 2006)
Page 17
There is an increasing variety of real-time, downhole, monitoring and measurement systems
which are now available for deployment ranging from simple system with sliding sleeves open or
shut, to high end hydraulic/electric systems with infinitely variable chokes and extensive
monitoring of pressure and temperature, sand detectors, multi-phase metering, resistivity and
seismic sensors for tracking near well fluid contacts. There are sensors for high resolution
pressure and temperature, high frequency pressure (acoustic), multiphase flow rate, phase-cut,
electric potential (electro-kinetic), seismic (accelerometers) and casing condition monitoring
(strain). A thorough understanding of what data is actually needed, the most suitable sensor types
and interfaces together with availability of the necessary data reconciliation and validation
methodology are key factors for the success of an integrated IW project (Silva et al., 2012).
2.4 CLASSIFICATION OF MONITORING SYSTEMS
Permanent well monitoring is divided into two classes of systems; deep reservoir and near
wellbore monitoring systems. Deep reservoir sensing is related to 4D seismic (time-lapse
seismic), dynamic 3D resistivity and streaming potential (electro-kinetic) – monitoring
techniques which capture the dynamics of the entire reservoir. Near wellbore sensing includes
the classical downhole measurements such as pressure and temperature (Silva et al., 2012).
Permanent sensors may also be classified by technology as electronic or optical and by the
number of monitored points as single point, quasi-distributed and distributed. Single point
sensors read the physical quantity to be monitored at a point; example is the permanent
downhole gauge (PDG); pressure is often monitored close to the reservoir depth or at the top of
the interval of interest. Quasi-distributed sensors allow the monitoring of the physical quantities
Page 18
at a distinct number of locations across the reservoir or interval of interest, it requires at least
three sensors measuring the same physical quantity to be installed at various points across each
interval. Distributed sensors monitor physical quantity at a spatial resolution as small as 0.5m;
example is the distributed temperature sensor (DTS). The distributed sensing network requires
understanding of all errors and limitations in the signal path besides the sensor itself (Silva et al.,
2012).
2.5 COMPONENTS OF IWC
The basic elements of IWC are as follows (Eni Group, 2006):
Acquisition and transmission system
Flow control valves (FCV) and
Actuation system
The acquisition and transmission system is a set of equipment usually electric or fiber optic, used
to transmit and acquire reservoir data.
Two types of flow control valves are available; binary control (on/off valve) and choke valve.
The on/off valve is used to open or close the flow rate in a level, hence has only two positions;
fully open or fully close and does not have the possibility to choke the flow. Internal control
valve (choke valve) has the capacity to choke the flow rate. There are two types available;
multistep and infinity variable choke. Multistep valve can have only a limited number of discrete
positions to choke the flow while the infinite variable valve can have a continuous regulation of
flow area from 0% to 100%. The selection of the right flow control is critical, as it may have an
Page 19
impact on the number of zones or intervals that can be effectively controlled in one well (Silva et
al., 2012).
The actuation system is a set of equipment that supplies power to the valves. There are three (3)
systems available, hydraulic, hybrid and electric system. The hydraulic system usually for on/off
and multistep valves, is the simplest system in the market and it is less expensive. The hybrid
system usually for on/off, multistep and infinity variable valves, is an electro-hydraulic system
and provides a higher level of control than the hydraulic system. Controls consist of hydraulic
power, electric distribution of the pressure and monitoring or control of the flow control valve
(FCV) positions. The hydraulic control moves the FCVs while the electric control communicates
signals (move solenoid valves). Compared to the hydraulic system, the hybrid system has a high
level of control and steers around failure but it is complex and expensive. For the electric system,
FCV can assume infinity positions to choke the flow rate. This system need only one electric
permanent downhole cable for multiple valves, the same cable transmits the signal from the
PDGs and the electrical power to the FCVs. The valve has inside pressure, temperature and
diagnostic measurements. Advantages of the electric system include high level of control and
number of control lines. Its disadvantages include no redundancy, high cost and level of
complexity (Eni Group, 2006).
2.6 APPLICATIONS AND BENEFITS OF IWC
Installation details or data acquisition issues need to be fully analyzed to design a robust,
integrated, monitoring architecture that delivers the full added value. Instrumentation and other
hardware advances have allowed more flexibility at the design and installation stage while
Page 20
enabling the systems use for a variety of new applications. For example, developments in
pressure and temperature gauges, allow control of the sample rate for capturing fast events
without causing data overload. Cableless technology for example allows access to data from
gauges installed within the laterals of a multilateral well. New hybrid (electric/hydraulic) IWC
systems are becoming available, enabling the flow control system and the sensor to share the
same system architecture. This represents a reduced number of control lines and allows
interchangeable modules for actuators and sensors, increasing the installation’s flexibility while
simplifying the installation procedure (Silva et al., 2012). IWC has been employed in many areas
to maximize recovery.
Areas of IWC application include the following:
Commingle production
Managing drawdown
Distribution of injectant (water and gas injection)
Water and gas coning
Sand control
Gas lift
Control WAG injection
Miscible flood
Minimal intervention
Page 21
The value of the IWC comes from the ability to actively modify the well zonal completions and
performance through flow control and to monitor the response and performance of the zones
through real-time downhole data acquisition. The benefits of IWCs will vary from different
fields and is a function of reservoir quality, production rates, fluid contacts, gas and water
coning, etc. The cost of a conventional intervention is also an important parameter. For each
reservoir installation of IWC, there are objectives for its installation hence the value of IWC
must meet the objectives as well as other benefits.
The primary objectives of IWs are generally to maximize or optimize production/recovery,
minimize operating costs, and improve safety (Robinson, 2007).
The most important benefit of a smart well system is improved reservoir management or
monitoring; ability to remotely choke or shut zones with poor performance will give an
immediate response on the well performance without any expensive well interventions. With
downhole sensors data will be collected for every zone along the well path continuously which
will be helpful for reservoir models and also give input for optimal openings of the inflow
control valves.
IW also reduces intervention costs, well intervention in horizontal wells is far more complex and
expensive both in terms of reservoir monitoring (production logging) and zonal isolation
(plugging or patching). Without any zonal control, the production potential from a long
horizontal well may be restricted by small thief zones.
In additional to their main application, they can also identify and classify failures in downhole
equipment (Hudson et al., 2011).
Page 22
General benefits of remote completion monitoring and control are as follows:
Provide quality reservoir data for support of total field development.
Improve zonal or areal recovery monitoring (locate remaining oil and define infill
development targets)
GOR and water-cut can be controlled by changing the position setting of the choke,
optimizing oil production from the different layers.
Water and gas injection rates can be regulated for individual sections of injection wells,
thereby optimizing the sweep efficiency.
Minimize or eliminate the need for well intervention (reduce intervention costs)
Target stimulation treatments from surface
Reduce water handling (reduce cost of surface facilities)
2.7 LIMITATIONS OF IWC
High reliability of IWC is necessary for avoiding workover operations; whereas monitoring and
measuring of downhole reservoir data and choking ability are also important to reach optimal
production.
Certain risks are common to any application of a downhole control system. Common risks
include cable or line failure particularly during installation, longer-term system failures may be
caused by erosion, temperature effects on electronics, wear and tear and seizure of moving
Page 23
components. The simpler the system and the fewer moving parts, the fewer components
available to fail.
Control lines, cables and sensors represent the nervous and circulatory system of an IWC and
damage to these elements may mean partial or total loss of the functionality of the IWC.
(Robinson, 2007)
Veneruso et al. (2000) in their study of reliability of permanent downhole equipment found that
the equipment reliability greatly depends on temperature; gauges in high temperature
environment have a shorter expected life time.
Page 24
CHAPTER 3
METHODOLOGY
3.1 INTRODUCTION
Oil and gas production operations involve risks as a result of uncertainties from reservoir
properties, equipment failure as well as unforeseen future events; hence evaluation and
justification of field development projects are extremely important, as projects incur huge sums
of money and once a field is developed, the whole architecture cannot be changed entirely.
This section describes the methodology used to compare the application of intelligent well
completions (IWC) vs. non-intelligent completions in reservoir management. In the presentation,
we consider data acquisition, oil recovery and the economics involved in both completions.
Case studies of reservoirs where intelligent completions have been employed were analyzed. The
tools used for this analysis include material balance software (MBAL) for production forecast
using decline curve analysis and Monte Carlo-Simulation-software (a spread sheet add-in, Oracle
Crystal Ball) for the uncertainty analysis of the economics of the two types of completions.
Figure 3.1 illustrates the major steps in the workflow which is patterned after the model proposed
by Sakowski et al, (2005); the detailed step-by-step procedure used in their work is illustrated by
Figure 3.2.
Page 25
Figure 3.1: Workflow used for reservoir and economic analysis of IWC
No Yes
Define possible application
and objectives of IWC
Estimate production profile
or performance
Carry out economic
analysis of project
Desired profitability
and objectives
achieved?
IWC project
justified
IWC project not
justified
Page 26
Figure 3.2: Detailed Step-by-Step Procedure for Reservoir and Economic analysis of IWC
application (Adapted from Sakowski, Anderson and Furui, 2005)
3.2 RESERVOIR DATA ACQUISITION AND MANAGEMENT
Reservoir management uses elements of geology, geophysics and petroleum engineering to
predict and manage oil recovery which requires a thorough knowledge of the reservoir through
an integrated efficient data management. So much data is collected and analyzed during the life
of a reservoir. Data acquisition and management is key to project success; hence must be
Yes
No
No
Yes
Identify a possible
application of IWC
Define architecture of basic and
IW completion
Simulate behavior of well
(Nodal analysis, Simulation)
Increase
production rate or
recovery factor?
Redefine architecture of
the IWC
Carry-out economic
analysis of project
Desire
profitability
achieved?
Execute the IWC
project
Consider other
alternatives
Page 27
carefully planned and guided by timeliness, quality and cost effectiveness. The need for the data
and associated cost or benefit analysis should be established.
The need and type of data obtained from IWC were compared to conventional or non-intelligent
wells, for the reservoirs studied in this work.
3.3 WELL PERFORMANCE
Well performance in various well configurations (vertical, horizontal wells, multi-lateral etc.)
was compared by analyzing oil production, water and gas production, for both intelligent and
conventional or non-intelligent completions.
3.3.1 INFLOW AND VERTICAL LIFT PERFORMACE
The ability of a well to produce fluids depends on the capacity of the piping system to carry these
fluids to the surface which is controlled by three flow processes; flow from reservoir to the well
bore (Inflow performance), flow from the well bore to the well head (Vertical lift) and flow
through chokes, flow lines and process facilities (Surface flow). Reservoir system analysis
relates pressure drop (ΔP) with flow rate and allows determination of the producing capacity for
any combination of components of the well by analyzing the inflow performance (IPR) and
outflow performance (VLP) relationships. The IPR curve describes the relationship between the
production rate across the reservoir-wellbore interface and the wellbore pressure across the
thickness of the producing zone. The tubing intake curve describes the relationship between the
bottomhole flowing pressure and the rate of flow through the production tubing, allows
evaluation of the friction losses developed through the production tubing as a function of the
fluid flow rates.
Page 28
From Darcy’s law, the flow equation for stabilized radial flow of a single phase liquid flow
through homogenous formation from an infinite reservoir is given by equation 3.1 and the
productivity index (PI), which is the ratio of flow rate to pressure drawdown is given by equation
3.2.
The simplest and most widely used IPR equation is the straight line IPR which is applicable for
undersaturated reservoirs but for two-phase flow or saturated reservoirs, Vogel’s equation given
by equation 3.3 and Standing equation which is a modification of Vogel’s equation, can be used
to describe the IPR.
For horizontal well, the inflow performance equations at steady-state have been proposed by
Borisov, Merkulov, Giger, Giger et al, Renard & Dupuy and Joshi. The most popularly used
model is the Joshi’s equation given by equation 3.4 for an anisotropic reservoir (kh is different
from kv) and equation 3.8 for an isotropic reservoir (kh=kv).
Page 29
where is given by equation 3.5
where c and b are the major and minor radii of a drainage ellipse. The PI is given by equation
3.7.
The pressure drop along the production tubing can be calculated by using charts or correlations.
Gradient or Transverse curves can be used to determine the wellbore flowing pressure at
different oil rates if the wellhead pressure is specified. Table 3.1 shows some correlations used in
the industry.
Page 30
Table 3.1: Some correlations used in the industry
Method Well Fluid Comments
Duns & Ros
(1972)
Oil, Water, Gas Optimistic, under predicts pressure drop
Beggs & Brill
(1973)
Oil, Water, Gas Use for deviated well greater than 45 degrees,
tends to over predict pressure drop, used for
horizontal wells
Gray Water, Gas Good for gas condensate wells
Cullender & Smith Water, Gas Used for dry gas wells
3.3.2 DECLINE CURVE ANALYSIS
Decline curve analysis is based on empirical relationship of production rate versus time given by
Arps in 1945. Arps presented three types of production rate-time decline, namely, exponential,
hyperbolic and harmonic decline equations (Table 3.2).
Table 3.2: Arp’s equations
In this work, exponential decline was used because it is known to give a more conservative
production rate forecast. For exponential decline the equation can be written as follows:
Page 31
where D is the exponential decline rate
In the above case, d is called the nominal decline rate and can be written as
The nominal and exponential decline rate can be related as;
The applicable decline for the purpose of reserves estimates is usually based on the historical
trend that is seen on the well or reservoir performance. It is assumed that the factors causing the
historical decline continue unchanged during the forecast period. These factors include both
reservoir and operating conditions. Operating conditions that influence the decline rate are;
separator pressure, tubing size, choke setting, workovers, artificial lift, operating hours,
compression etc. For instance the decline rate determined pre-workover will not be applicable to
the post-workover period.
3.3.3 PRODUCTION RATE FORECAST
The analysis of production rate versus time was carried out using MBAL. MBAL is a Petroleum
Experts software package which is made up of various tools such Material Balance, Reservoir
Allocation, Monte Carlo Volumetric Analysis, Decline Curve Analysis, 1-D Model and Multi-
Page 32
layer to perform prediction runs, but this work only employed Decline Curve analysis to forecast
well production rate versus time.
Daily oil production history from both intelligent and non-intelligent wells was history matched
using exponential decline and the future well production rates versus time were calculated from
MBAL performance prediction runs.
3.4 TIME VALUE OF MONEY AND ECONOMIC ANALYSIS
Decision-making in investment analysis requires anticipated revenues and cost of investment
alternatives to be placed on equivalent basis. As a result of interest rate, inflation and risk
investment today may not be of the same value tomorrow. Economic analysis of each scenario
was done based on the time value of money by comparing the after tax cash flow in each case;
considering the CAPEX, OPEX and some profitability indicators such as discounted payout
period, net present value, profitability index and growth rate of return. Also, uncertainty analysis
was performed to assess the project (IWC versus non-intelligent completion) risks. Net-present
value methods recognize the time value of money and are critical when assessing the profitability
of long-term investments (Main, 2002). After-tax Net cash-flow given by equation 3.13 is the
cash received less cash spent during a period. The cash inflow is basically from the revenue
generated from the sale of oil and gas. The gross revenue is given by equation 3.14.
Page 33
Inflation was considered in the cash flow analysis. Inflation decreases the purchasing power of
money. The price of a product increases by inflation rate at time t from present time and it is
given by:
where Ao, %ri and t in Equation 3.15 are the base price, the inflation rate, and the years measured
from the present time, respectively.
3.4.1 OPEX
Operating expenditure (OPEX) also called lease operating expenditure is the direct cost
associated with production or injection. OPEX includes fixed operating cost e.g., management
fees, and variable operating costs which include utilities, maintenance, production costs, etc.
3.4.2 CAPEX
Capital Investment (CAPEX) refers to as front-end cost is the capital invested in assets that will
generate benefits for more than one year. These include cost of drilling and developing wells,
surface equipment, completion, installing facilities for enhanced recovery, etc. CAPEX consists
of tangible such as surface equipment cost, etc and intangible CAPEX such as seismic
acquisition, etc.
Page 34
3.4.3 PROFITABILITY INDICATORS
The profitability indicators used in this work are described in the following section.
3.4.3.1 NET PRESENT VALUE (NPV)
Net present value is the most popular petroleum evaluation criterion. NPV is obtained by
subtracting the present value of periodic cash outflows from the present value of periodic cash
inflows. For end of year discounting, NPV is given by:
For mid-year discount factor, NPV is given by;
This study adopted the year end discounting method for the NPV calculations.
3.4.3.2 PROFITABILITY INDEX (PI)
PI measures the efficiency of an investment. It is a dimensionless ratio which is obtained by
dividing the present value of future operating cash flows by the present value of the investment.
Mathematically, the PI is given by the following equations.
Page 35
where
3.4.3.3 GROWTH RATE OF RETURN (GRR)
This is also called equity rate of return or modified internal rate of return (MIRR). A project is
desirable if the GRR is greater than the hurdle rate, rd.
For annual and continuous compounding, the GRR is given by equation 3.21 and 3.22
respectively.
3.4.3.4 PAYOUT PERIOD (PO)
Payout is the time required to recover the investment either before income tax or after income
tax. We have used after income tax payout time in this work. Cash receipts are exactly equal
investment at this point.
The payout period can be calculated by accumulating the negative net cash flow each year until it
turns positive or by plotting the cumulative net cash flow versus time, the intersection of the time
line at zero net cash flow is the payout period. The partial payback in the year when the NCF
turns positive can be calculated using equation 3.23.
Page 36
3.4.4 MONTE-CARLO SIMULATION
Sensitivity is the amount of uncertainty in a forecast caused by model assumptions and input data
uncertainties. Monte Carlo simulation aids in making predictions by accounting for randomness
and future uncertainties through investigation of different input data scenarios. Sensitivity
analysis was performed on variables considered to be estimates with high uncertainties such as
oil price, oil price inflation rate, discount rate, etc. Oracle Crystal Ball software was used to
determine the impact of variations in the input variables on the base case value of profitability.
Page 37
CHAPTER 4
RESULTS AND DISCUSSION OF CASE STUDIES IN RESERVOIR MANAGEMENT
4.0 INTRODUCTION
The results obtained from the analysis are presented and discussed in this Chapter. Sample
worked examples are presented in the Appendix.
Actual field cases where IWC has been proposed and implemented as solution to the challenges
in reservoir management in the field were evaluated and compared with non-intelligent
completions.
4.1 ASSUMPTIONS COMMON TO ALL FOUR CASE STUDIES
In this work, the following assumptions were common to all the four case studies considered.
Periodic year-end funds flow
Royalty
o Cumulative oil production less than 1MMSTB – 5%
o Cumulative oil production greater than 1MMSTB – 12.5%
Depreciation method - Double declining depreciation
Oil price inflation - 2.5%
Income Tax – 45%
Discount rate – 12.5%
Page 38
20% of CAPEX is expensed
Table 4.1 shows the input variable stochastic distributions common to all four case studies.
Table 4.1: Variable distribution input
Parameter Distribution Minimum Most
likely
Maximum Mean Standard
deviation
Oil price inflation, % Normal 2.5 1.6
Income Tax, % Uniform 25 50
Discount rate, % Triangular 10 12.5 15
4.2 CASE STUDY 1: COMMINGLED PRODUCTION FROM A TWO-LAYER
OFFSHORE FIELD
The first case study is the application of IWC in an offshore field presented by Behrouz et. al.
(2010). The structure of the reservoir is anticline, with two-layer sand stone formation separated
by an impermeable shale layer. The reservoir has no gas cap but has a strong aquifer with edge
water drive mechanism. Out of seven wells, three wells were proposed for intelligent completion
with commingled production. (Behrouz et. al, 2010)
The total oil production from the intelligent and conventional wells for six years was compared,
and the economics of the project analyzed.
Assumptions made in the analysis of Case Study 1 include the following:
Page 39
Drilling and completion cost for each well is $7MM
Equipment and installation cost of IWC without ICV is $1MM
ICV unit cost is $0.5M
Period considered is six years
Workover cost per year is $1.0MM
No workover was carried out in the IWC over the six years
Operating cost is $4/bbl without workover
Additional input data used in the analysis for this case study are listed in Table 4.1.
Table 4.2: Stochastic variable input parameter distribution for Case Study 1
Parameter Distribution Minimum Most likely Maximum
Capex IWC, MM$ Triangular 8 9 11
Capex CW MM$ Triangular 6 7 9
Workover cost per year, MM$ Uniform 0.5 2.5
Reservoir management
The intelligent completion (IWC) allowed reservoir monitoring of pressure drawdown and
individual layer productivity test. Layer 2 was shut in as a result of high water cut confirmed by
Page 40
the reservoir monitoring. It was observed that the oil production from Layer 1 increased due to
elimination of back pressure exerted by the high water production rate from the Layer 2. Figure
4.1 shows the total oil production from the conventional and intelligent wells for six years.
The use of IWC increased oil production by 23.9% compared to the convention well (CW),
which can be seen in Table 4.3. This oil production increment can be attributed to the
optimization of oil production in the IWC by the use of downhole flow control valves.
Figure 4.1: Production performance from the IWC and the conventional well
0
500
1000
1500
2000
2500
0
50
100
150
200
250
300
350
400
450
500
0 2 4 6
Cu
mu
lati
ve o
il p
rod
uct
ion
, Mb
bl
An
nu
al o
il p
rod
uct
ion
, Mb
bl
Time, year
Oil production versus time
CW oil production IWC oil production
IWC Cumulative oil production CW Cumulative oil production
Page 41
Economic analysis
The NPV in both completions was positive which means that both projects are acceptable but the
IWC gave a higher NPV compared to the conventional well. From the certainty analysis, the
NPV of the IWC at P90, P50 and P10 compared to that of the conventional completion gave an
increment of 25.6%, 27.5% and 30.1% respectively (Table 4.3). Hence the IWC improved the
economic outcome of the well and these results can be attributed to the accelerated oil
production and reduced well intervention costs from the IWC. This makes the IWC preferable to
conventional completion
Figure 4.2 shows the sensitivity analysis on the NPV in both completions. From Figure 4.2, it
can be observed that oil price and oil price inflation have a positive impact on the NPV whiles
Income Tax, workover cost per year and CAPEX have a negative impact on the NPV. Therefore,
increase in oil price and oil price inflation will increase the NPV whiles an increase in Income
Tax, workover cost per year and CAPEX will lower the NPV. The Tornado chart in Figure 4.3
ranks the most sensitive parameter to the least sensitive. From Figure 4.3, the oil price is the
most sensitive to the NPV in both completions. Hence, the decision to implement IWC or not
will depend on the prevailing oil price because the economic performance relies greatly on the
revenue generated from the oil sale.
Although the CAPEX in IWC was higher, the operating cost was less than that of the
conventional completion by 29%.
The payout period illustrated by Figure 4.4 was less than seven (7) months in both completions.
This shows that investment in both completions is recovered early enough, considering the
economic life of six years. The conventional completion payout period was slightly earlier
Page 42
compared to that of the IWC. This means that IWC project is riskier compared to the
conventional completion project, especially for a politically unstable country.
The PI and the GRR was almost the same for both completions, a difference of less than 1%. The
PI was more than one and the GRR greater than the hurdle rate of 12.5% in both completions.
This shows that both projects are acceptable. Summary of results obtained from this analysis is
presented in Table 4.3.
Since the PI, GRR and PO are almost the same for both completions, IWC installation can be
justified based on incremental oil recovery and NPV. IWC installation is justifiable, since it
improved recovery and the economic outcome of the well.
Figure 4.2: NPV sensitivity analysis for both IWC and conventional well
Page 43
Figure 4.3: Tornado charts of the NPV for the IWC and the conventional well
Figure 4.4: Cumulative discounted net cash flow versus time
64
27%
0.45%
11.12%
8.55
96
47%
4.55%
13.88%
10.23
30 40 50 60 70 80
Oil price
Income Tax
Oil price Inflation
Discount rate
CAPEX
NPV - IWC Downside
Upside
64
27%
0.45%
0.7
11.12%
6.55
96
47%
4.55%
2.3
13.88%
8.23
20 30 40 50 60 70
Oil price
Income Tax
Oil price Inflation
Workover cost per year
Discount rate
CAPEX
NPV - NON IWC Downside
Upside
-10
0
10
20
30
40
0 1 2 3 4 5 6 7
Cu
mu
lati
ve
Dis
cou
nte
d N
CF
,
MM
$
Time, year
Payout period for both completions
IWC
CW
Page 44
Table 4.3: Summary of results for Case Study 1
Parameter IWC CW Difference (%)
Cumulative Oil produced, MMbbl 1.992 1.607 +23.9
Deterministic Economic Results
NPV, MM$ 36.83 29.58 +25.9%
Operating cost, $/bbl produced 8 11 -27.2%
OPEX, MM$ 5.98 10.82 -44.8%
CAPEX, MM$ 9.00 7.00 +28.6%
DPO, months 7.20 6.00 +1.20 months
PI 2.01 2.03 -0.99%
GRR, % 26.38% 26.60% -0.22%
Stochastic Results of NPV, MM$
P90 43.15 33.16 +30.1%
P50 58.06 45.54 +27.5%
P10 76.28 60.75 +25.6%
Page 45
4.3 CASE STUDY 2: MULTI-LATERAL PRODUCER-INJECTOR PATTERN
OFFSHORE FIELD
The second case study is a mature field located in a low permeability limestone environment.
This case study is taken from the study of Ajayi et al. (2006). Multi-lateral wells, producer-
injector pattern were employed to enhance oil production and maintain reservoir pressure, but
this lead to high water production. Some of the multi-lateral producers recorded as high as 99%
water-cut with a field average water-cut of 75% which drastically reduced oil production rate.
One multi-lateral oil producer with four branches was selected for a trial to evaluate the use of
IW technology to restore field oil production, decrease water production, improve water flood
efficiency and to increase reservoir knowledge. The well was shut in after three and half (3.5)
years of production to install the IWC. The intelligent completion design was made up of four
inflow control valves (ICV) attached to each lateral. Each lateral was separated by isolation
packers to ensure monitoring of the individual layers. (Ajayi et. al, 2006)
In this study two scenarios were considered for this case. Evaluation of performance of the well
for a period of 10 years if;
1. No well control system was installed
2. Well control (intelligent system) was installed after three years of production
Production history from the non-intelligent completion and the IWC were history matched using
exponential decline non-linear regression with medium data point weighting and production
forecasted for 10 years. The production history from the IWC and non-intelligent completion are
shown in Figure 4.5.
Page 46
Figure 4.5: Oil production history from both completions (Ajayi et. al, 2006)
Assumptions used in the analysis of Case Study 2 are listed below and Table 4.4 contains the
distribution of the stochastic input variables.
Drilling and completion cost is $3.5MM
Well equipment cost is $4.0MM
ICV unit cost is $0.5MM
Intelligent system installation cost is $1MM
Operating cost without workover is $3/bbl
Workover cost per year is $1.0MM
0
1000
2000
3000
4000
5000
6000
7000
0 2 4 6 8
Oil
Rat
e, b
bl/
day
Time, year
Oil production history from both completions
IWC
Non-IW
Page 47
Table 4.4: Stochastic variable distribution of input parameters for Case Study 2
Parameter Distribution Minimum Most likely Maximum
CAPEX IWC, MM$ Triangular 9 10.5 12
CAPEX NON-IWC, MM$ Triangular 6 7.5 9
Workover cost per year, MM$ Uniform 0.5 2.5
Reservoir management
Downhole sensors in the IWC allowed real time monitoring of pressure drawdown and
production from each lateral; the observed data was used to optimize the inflow from each
lateral. The productivity index for each lateral was established without extra cost. Zones 1 and 4
were found to produce a larger fraction of total water produced, hence the ICV for zones 1and 4
were set to fully closed and the well was produced only from branches 2 and 3.
Oil production increased by 21% (Table 4.5) in the IWC compared to the non-intelligent
completion. This is due to the optimization of oil production in the IWC by the use of downhole
control valves. Figure 4.6 shows the oil production performance for 10 years in both
completions.
Page 48
Figure 4.6: Oil production performance for the two scenarios
Economics Analysis
For both completions, the NPV was positive, the PI was greater than one, the GRR was greater
than the hurdle rate and the payout period was desirable. This shows that both projects are
acceptable but the economic outcome from the IWC was more attractive compared to that of the
non-intelligent completion.
From the stochastic analysis, the NPV for the IWC was higher that of the non-intelligent
completion. The IWC gave 17.5%, 18.5% and 20% increase in NPV at P10, P50 and P90
respectively (shown in Table 4.5). This shows that the IWC investment gave more value
compared to the non-intelligent completion. The increase in NPV in the IWC can be attributed to
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 2 4 6 8 10 12
Cu
mu
lati
ve o
il p
rod
uce
d, M
MST
B
An
nu
al o
il p
rod
uct
ion
, MM
STB
Time, year
Oil production performance for both completions
IWC - Annual oil production NON-IWC - Annual oil production
NON-IWC - Cumulative oil produced IWC - Cumulative oil production
Page 49
the increase in oil recovery and less operating cost (decreased by 23.3%). The high workover
cost in the non-intelligent completion gives rise to the high operating cost.
Figure 4.7 shows the sensitivity of oil price, income tax, oil price inflation, workover cost per
year, discount rate and CAPEX on NPV in both completions. It can be observed that oil price
and oil price inflation have a positive impact on NPV whiles the income tax, workover cost per
year and discount rate have a negative impact on NPV. This shows that increase in oil price and
oil price inflation increases the NPV but increase in income tax, workover cost and discount rate
will decrease the NPV. Figure 4.8 illustrates the Tornado chart for both completions. The
Tornado chart ranks the most sensitive variable to the least. It can be observed that oil price is
the most sensitive and CAPEX the least sensitive in both the IWC and the non-intelligent
completion. Therefore a lower oil price may render both projects less attractive.
Figure 4.9 illustrates the payout period in both completions. The payout period was less than
three (3) months in both completions. This shows that investments in both projects for 10 years
of production are recovered early enough and have the same risk exposure.
The total return on investment dollars was $3.05 per dollar for the IWC whiles that of the non-
intelligent completion was $2.73 per dollar. This shows an increase in PI by 11.7% in the IWC
compared to the non-intelligent completion. This makes the IWC more preferable.
The project worth plus the reinvestment opportunities for the IWC was 25.78% whiles that of the
non-intelligent completion was 24.36%, giving a difference of 1.42%. This shows that the
investment in IWC was better than that of non-intelligent completion.
Page 50
Since the objectives of the IWC installation-to reduce water production, increase oil recovery
and ease acquisition of reservoir data were met in addition to improved economic outcome, IWC
implementation is justified.
Figure 4.7: NPV sensitivity analysis for both completions
Figure 4.8: Tornado chart of the NPV for both wells
64
27%
0.45%
11.1%
0.7
9.67
96
47%
4.55%
13.9%
2.3
11.33
80 100 120 140 160 180
oil price
Income Tax
Oil price Inflation
Discount rate
Workover cost per year
CAPEX
NPV IWC Downside
Upside
64
27%
0.45%
0.7
11.1%
6.67
96
47%
4.55%
2.3
13.9%
8.33
60 80 100 120 140 160
oil price
Income Tax
Oil price Inflation
Workover cost per year
Discount rate
CAPEX
NPV - NON IWC Downside
Upside
Page 51
Figure 4.9: Cumulative discounted net cash flow versus time
Table 4.5: Summary of results for Case Study 2
Parameter IWC Non-IWC Difference (%)
Cumulative Oil produced, MMbbl 4.224 3.475 +21.6%
Deterministic Economic Results
NPV, MM$ 85.21 71.63 +19.0%
OPEX, MM$ 15.67 20.42 -23.3%
CAPEX, MM$ 10.50 7.50 +40.0%
DPO, months 2.04 2.04 0
PI 3.05 2.73 +11.7%
GRR, % 25.78% 24.36% +1.42%
-10
10
30
50
70
90
0 2 4 6 8 10 12 Cu
mu
lati
ve D
isco
un
ted
NC
F, M
M$
Time, year
Payout period for both completions
IWC
NON-ICW
Page 52
Stochastic Results of NPV, MM$
P90 99.18 82.62 +20.0%
P50 131.07 110.65 +18.5%
P10 169.84 144.54 +17.5%
4.4 CASE STUDY 3: TRIPLE COMINGLED PRODUCTION – USARI FIELD
OFFSHORE NIGERIA
The third case study is taken from the Usari field located about 16 miles offshore Nigeria in
about 72 feet of water (Brock et al., 2006). The field has 35 reservoirs which are subdivided into
shallow (18 reservoirs), intermediate (15 reservoirs) and deep (2 reservoirs) based on the fluid
properties, pressure regimes and the geologic setting. The Usari shallow reservoirs are made up
of faults sealed within a graben. The graben has seven reservoirs which were discovered in 2001.
Development wells were planned to be completed across three (3) of the seven reservoirs (Figure
4.10), the 7-US1G which is the upper reservoir, the 8-US1G which is the middle reservoir and
the 9-US1G, the lower reservoir as a triple comingled producer using intelligent well completion.
The well in Figure 4.10 was made up of multi-position flow control valves for each of the three
9-5/8 inch gravel packed completion. The flow control valves can be activated at the surface and
had permanent downhole pressure and temperature gauges in front of the valves (Brock et al.,
2006).
Page 53
Figure 4.10: Development well path (Brock et al., 2006)
Daily production history from the IWC was history matched using exponential decline, non-
linear regression analysis with medium weighted data points and the oil production, forecasted
for six years. Figure 4.11 shows the daily production history from the IWC.
Figure 4.11: Production history from the IWC for Case Study 3 (Brock et al., 2006)
Page 54
Assumptions used in the analysis of Case Study Number 3 include the following:
Analysis period was six years and the life of the intelligent system equipment can last
over the six years period, hence no workover cost inquired.
Well equipment cost is $3.0MM
Rig cost is $95,000/day
Intelligent equipment and installation cost without ICV is $1.0MM
Unit cost of ICV is $0.5MM
Production and handling cost without workover cost is $5/bbl
Triangular distribution is assumed as the probability distribution for the CAPEX
Reservoir management
Real time pressure and temperature profiles are being recorded and monitored by use of the
intelligent system. Build up test and productivity test can be done simultaneously without
shutting in the well by the use of the FCV and the result is simulated to optimize production.
Geochemical analysis of oil sample from each zone was also made possible by use of the
intelligent system without additional cost. Figure 4.12 shows the production forecast for six (6)
years.
Page 55
Figure 4.12: Oil production forecast for Case Study 3
Economic analysis
A project can be acceptable if the payout period is desirable, the NPV is positive, the PI greater
than one and the GRR greater than the hurdle rate. From the stochastic analysis, the NPV at P90
for the IWC installation was 189.56MM$ which shows the value of the investment for six years.
This high NPV can be attributed to high production from the optimized flow and no intervention
cost incurred.
Figure 4.13 shows the Spider and Tornado charts of the NPV from the IWC. The Spider chart
shows the most sensitive variable to NPV with the steepest line and the Tornado chart ranks the
most sensitive variable to the least sensitive variable to NPV. From the charts, the NPV is most
sensitive to oil price and CAPEX is the least sensitive. The sensitivity analysis in Figure 4.13
shows that the oil price and oil price inflation have a positive impact on the NPV whiles the
income tax and discount rate have a negative impact on the NPV, as observed from previous case
0
2
4
6
8
10
0.0
0.5
1.0
1.5
2.0
0 2 4 6 8 Cu
mu
lati
ve o
il p
rod
uct
ion
, MM
bb
l
An
nu
al o
il p
rod
uct
ion
, MM
bb
l
Time, year
Oil production from the IWC
Annual oil production Cumulative oil production
Page 56
studies. Therefore increase in oil price will increase the NPV whiles increase in discount rate and
income tax will decrease the NPV.
The payback period illustrated by Figure 4.15 was less than four (4) months. This shows that for
six years of production, investment is recovered early, which reduce the risk exposure of the
investment.
The total return on the investment dollars was $4.92 per dollar which is acceptable. The GRR
was far greater than the hurdle rate of 12.5% which shows that the worth of the project plus the
reinvestment opportunities is also acceptable. Table 4.8 gives the summary of the results
obtained for this case study.
Since IWC installation is very economical and allows reservoir optimization, IWC
implementation is recommendable.
Figure 4.13: Spider and Tornado charts of the NPV
150
200
250
300
90% 70% 50% 30% 10%
Percentiles of the variables
NPV IWC
Oil price
Income Tax
Oil price Inflation
Discount Rate
CAPEX
64
27%
0.45%
11.1%
9.61
96
47%
4.55%
13.9%
11.28
150 200 250 300 350
Oil price
Income Tax
Oil price Inflation
Discount Rate
CAPEX
NPV IWC Downside
Upside
Page 57
Figure 4.14: Sensitivity analysis of the NPV
Figure 4.15: Cumulative discounted net cash flow versus time
-20
0
20
40
60
80
100
120
140
160
180
0 1 2 3 4 5 6 7
Cu
mu
lati
ve D
isco
un
ted
NC
F, M
M$
Time, year
Payout period for IWC implementation
Page 58
Table 4.6: Summary of results obtained for Case Study 3
Parameter IWC
Cumulative oil produced, MMbbl 8.770
Deterministic Economic Results
NPV, MM$ 162.75
OPEX, MM$ 43.85
CAPEX, MM$ 10.25
DPO, months 3.24
PI 4.92
GRR, % 46.70%
NPV stochastic results, MM$
P90 189.56
P50 252.30
P10 330.72
Page 59
4.5 CASE STUDY 4: HORIZONTAL WELL PRODUCTION – OSEBERG FIELD
OFFSHORE NORWAY
The fourth case study presented in this work is taken from the Oseberg field located offshore
Norway. The field is made up of three (3) main reservoir zones; the uppermost Tarbert
formation, the middle Ness formation and the base, the Oseberg/Rannoch/Etive (ORE) formation
(Erlandsen, 2000). As a result of the high gas-oil ratio in the deviated wells, the wells were
converted into horizontal well. The initial oil column of more than 200m has reduced to 20 –
40m oil rim; hence most of the production wells have experienced early gas break through and
reduction in the oil production rate. IWC was employed to control the gas production in order to
increase the oil rate (Erlandsen, 2000).
Intelligent system, electric-hydraulic control was installed in four wells; B-30B, B-21B, B-41A
and B-29B with the ICV in four (4) positions; 1/3 and 2/3 openings, fully opened and fully
closed, purposely to prevent early gas breakthrough. (Erlandsen, 2000)
Only two of the wells were analyzed in this study, B-30B and B-21B, to evaluate well
performance of IWC versus non-intelligent completion (Non-IWC).
Assumptions used in the analysis of the Oseberg Field include the following:
Drilling and completion cost is $8MM
Intelligent system equipment and installation cost without ICV is $1.5MM
ICV unit cost is $0.5MM
Workover cost is $1.0MM
Page 60
Production and handling cost without workover is $3/bbl
Table 4.7 lists additional input data used in the study of the Oseberg field.
Table 4.7: Stochastic variable distribution of input parameters of Case Study 4
Parameter Distribution Minimum Most likely Maximum
CAPEX IWC, MM$ Triangular 10 11 12
CAPEX NON-IWC, MM$ Triangular 7 8 9
The analyses of the data for the two wells are presented in the following section.
4.5.1 Well B-30B
Erlandsen reported that the position indicator of the IWC failed during installation; hence there
was no additional benefit other than zonal testing, temperature and pressure profile monitoring.
This shows that even when the downhole control valves in IWC fail, there are auxiliary benefits
from the use of IWC (data acquisition and monitoring). But when the well was close to dying
due to the increasing water-cut, the remotely operated zone was closed. This reduced water
production and increase oil production from the other zones.
Economic analysis
The additional capital expenditure of $2.0MM (25% increment in CAPEX) for the IWC
installation, it cannot be ascertain whether it significantly improved the NPV due to lack of
production data from this well.
Page 61
Although there was an increase in oil production at the later stage of the life of the well, the
objective of IWC installation was not achieved.
Since the IWC installation did not meet all the objectives for which it was installed as a result of
equipment failure, IWC installation in well B-30B cannot be justified.
4.5.2 Well B-21B
Reservoir management
From the analysis of this well, the total oil produced for five years was 4.416MMbbl from the
IWC whereas that of the non-intelligent well was 3.198MMbbl. This shows oil production
increment in the IWC by 38% compared with the production from the non-intelligent
completion. This can be attributed to the optimization of production based on the real time
productivity index, gas-oil ratio, water-cut, shut in pressure, flowing tubing pressures and
flowing annulus pressured for each zone obtained from the IWC. Figure 4.16 shows the oil
production in both completions.
Page 62
Figure 4.16: Production performance in both wells
Economic analysis
The NPV from both completions was positive, which shows that both projects are acceptable.
But the IWC gave a higher NPV compared to the non-intelligent completion. From the certainty
analysis, the NPV of the IWC at P90, P50 and P10 compared to that of the non-intelligent
completion gave an average increase of 39.9% (shown in Table 4.8). This is can be attributed to
the increase in oil production and reduced operating cost (reduced by 9.2%) from the IWC. With
reference to NPV, IWC is preferable to non-intelligent completion since IWC show a remarkable
increase.
Figure 4.17 shows the Tornado charts for both completions. From the Tornado chart, the oil price
is the most sensitive to the NPV and CAPEX is the least sensitive to the NPV as it has been
observed from all the previous cases analyzed. Therefore lower oil price reduces the NPV which
0
1
2
3
4
5
0
1
2
3
0 2 4 6
cum
ula
tive
oil
pro
du
ced
, MM
bb
l
An
nu
al o
il p
rod
uct
ion
, MM
bb
l
Time, year
Oil production versus Time
IWC Annual oil production NON-IWC Annual oil production
NON-IWC Cumulative oil produced IWC Cumulative oil produced
Page 63
may render the project economically unattractive because the economic performance relies
greatly on the revenue generated from the oil sale. The sensitivity chart in Figure 4.18 also shows
that oil price and oil price inflation have a positive effect on the NPV whiles the income tax and
discount rate negatively affect the NPV.
The payout period for both completions was less than two (2) months which is illustrated by
Figure 4.19. This shows that for five years of production, it takes less than two months to recover
the investment which is desirable. Thus both projects have a low risk exposure. The payout
period from the non-intelligent completion was slightly earlier compared to that of the IWC, but
because the payout in both projects is desirable, it does not make the non-intelligent completion
preferable to the IWC. The later payout period in the IWC can be attributed to the high cost of
IWC installation.
The PI from the IWC was 3.37 while that from the non-intelligent completion was 3.45. This
shows a decrease of 0.08 in PI using the IWC. IWC shows a slightly lesser PI due to the high
cost associated with it.
The GRR from the IWC was less than that from the non-intelligent completion by 0.7%. Base on
the difference of 0.7% in GRR, non-intelligent completion is preferable. But on the other hand,
the difference in GRR for both completions is less than 1%. The decision to implement IWC or
not cannot be based only on GRR, since both completion gave almost the same GRR. Summary
of the results obtained for Well B-21B analysis is given by Table 4.8.
Since IWC installation gave a higher oil recovery, increased the NPV within a desirable payout
period, IWC installation in Well B-21B is justifiable.
Page 64
Figure 4.17: Tornado charts of the NPV for both wells
Figure 4.18: Sensitivity on NPV for both completions
64
27%
0.45%
11.1%
10.67
96
47%
4.55%
13.9%
12.33
100 150 200 250
Oil price
Income Tax
Oil price Inflation
Discount rate
CAPEX
NPV - IWC Downside
Upside
64
27%
0.45%
0.7
11.1%
7.55
96
47%
4.55%
2.3
13.9%
9.23
60 80 100 120 140 160
Oil price
Income Tax
Oil price Inflation
Workover cost per year
Discount rate
CAPEX
NPV - NON IWC Downside
Upside
Page 65
Figure 4.19: Cumulative discounted net cash flow versus time for both wells
Table 4.8: Summary of the results for Well B-21B
Parameter IWC Non-IW Difference (%)
Cumulative oil produced, MMbbl 4.416 3.198 +38.0%
Deterministic Economic Results
NPV, MM$ 97.03 69.87 +38.9%
OPEX, MM$ 13.25 14.59 -9.2%
CAPEX, MM$ 11.50 8.00 +43.8%
-10
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 Cu
mu
lati
ve D
isco
un
ted
NC
F, M
M$
Time, year
Payout period for both completions
IWC
NON IWC
Page 66
DPO, months 1.92 1.68 +7.3days
PI 3.37 3.45 -2.3%
GRR, % 43.44% 44.14% -0.7%
Stochastic Results of NPV, MM$
P90 114.16 81.21 +40.57%
P50 151.03 107.9 +39.97%
P10 195.48 140.52 +39.11%
Page 67
CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS
5.1 SUMMARY AND CONCLUSIONS
This study compares the application of intelligent well completions versus non-intelligent or
conventional well completions for reservoir management. The objectives of this study is to
present a methodology which guides the planning and decision making process of implementing
IWC, as well as to present the benefits and limitations of IWC.
The case studies from four fields where IWC has been implemented were analyzed to examine
the applications of IWC. Daily production history from both IWC and non-intelligent completion
was history matched and prediction runs carried out. The criteria for judging the feasibility of
implementing IWC includes ease of data acquisition and well monitoring, incremental oil
recovery, NPV, Discounted payout period, Profitability Index and Growth Rate of Return.
Based on the results of the analysis of the case studies presented in this work the following
conclusions are drawn:
1. Incremental oil recovery from IWC vs. Non-IWC ranges from 21.6% to 38%.
2. IWC proves to be more economically viable compared to Non-IWC; NPV from IWC
exceeds that from Non-IWC by 17.5% to 40.6% for the case studies evaluated in this
work.
3. NPV is dependent on oil price.
4. Comparing field operating costs of IWC vs. Non-IWC shows that IWC OPEX can be
reduced from 9% to 45%.
Page 68
5. Payout time for IWC is a week to a month less than that of the Non-IWC but desirable.
6. IWC implementation is justifiable for the cases considered, except for IWC installation in
well B – 30B (Oseberg field) which did not yield the expected benefits due to the failure
of the surface-controlled downhole flow control valves. Failure of downhole control
devices would limit the profitability and justification for IWC projects.
5.2 RECOMMENDATIONS
Based on methodology and results of this study, the following recommendations are suggested:
1. Decline Curve Analysis was used for the oil production forecast in this work. It is
recommended that reservoir simulation be used for production forecast in similar
analysis.
2. Production period of five to six years after IWC implementation was considered in this
work. The entire life of the field, from IWC implementation to abandonment should be
considered in quantifying the benefits of IWC.
3. Statistics of all intelligent completions implemented, with their level of success, the risk
of failure, types of failure and the vendors should be compiled to guide the planning
process and actual field deployment of intelligent completion.
4. Pre-installation or pre-development test should be carried out to ascertain the reliability
of the intelligent system.
5. More research should be done on the intelligent system hardware to improve its
reliability.
Page 69
NOMENCLATURE
FCV Flow control valve
ICV Inflow control valve
S Skin factor
ko Effective permeability to oil, md
kv Effective permeability in the vertical direction, md
kh Effective permeability to oil in the horizontal direction, md
re External boundary radius, ft
rw Wellbore radius, ft
reh Effective or apparent wellbore radius, ft
Bo Oil formation volume factor, Bbl/Stb
h Pay thickness, ft
µo Oil viscosity, cp
ΔP Pressure change, psi
Pe External boundary pressure, psi
Pwf Flowing bottomhole pressure, psi
Average reservoir pressure, psi
Page 70
qo Oil production rate, bbl/day
qh Horizontal production rate, bbl/day
qmax Maximum production rate, bbl/day
Jh Horizontal productivity index, bbl/day/psi
L Length of horizontal section, ft
r Discount rate, percentage
I Interest payment on debt loan, percentage
Tc Corporate tax rate, percentage
NCFATAX Net cash flow after Income tax
NCFBTAX Net cash flow before Income tax
DD&A Depreciation expense
Page 71
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Page 76
APPENDIX
APPENDIX A: SAMPLE ECONOMIC ANALYSIS CALCULATION
For Case Study 1:
Table A1: NPV calculation for IWC
Time Annual Cum Oil Price Oil price Gross Royalty Net CAPEX OPEX Depreciation
(Year) Production Produced Deflator Revenue
Revenue
(bbl) (bbl) $/bbl MM$ MM$ MM$ MM$ MM$ MM$
0 0 0 1.00 60.0 0 0 0 9 0.00
1 470192 470192 1.03 61.5 28.92 1.45 27.47 1.41 3.60
2 286876 757068 1.05 63.0 18.08 0.90 17.18 0.86 2.16
3 291770 1048838 1.08 64.6 18.85 2.36 16.50 0.88 1.30
4 315587 1364425 1.10 66.2 20.90 2.61 18.29 0.95 0.78
5 314439 1678864 1.13 67.9 21.35 2.67 18.68 0.94 0.47
6 313533 1992397 1.16 69.6 21.82 2.73 19.09 0.94
TOTAL 1992397 5.98
Page 77
Table A1: NPV calculation for IWC (Continuation)
Total Tax Loss Taxable Income After Tax Cum. Discounted
Deduction Income Tax, MM$ NCF NCF NCF
MM$ MM$ MM$ 45% MM$ MM$ MM$
1.80 (1.80) (9.00) (9.00) (9.00)
5.01 (1.80) 20.66 9.30 16.76 7.76 14.90
3.02 14.16 6.37 9.95 17.71 7.86
2.17 14.32 6.45 9.17 26.89 6.44
1.72 16.56 7.45 9.89 36.77 6.17
1.41 17.27 7.77 9.96 46.74 5.53
0.94 18.15 8.17 9.98 56.72 4.92
Total 36.83
Table A2: NPV calculation for Conventional Completion
Annual Cum Oil Deflator Oil price Gross Royalty Net CAPEX OPEX Depreciation
Time Production Produced Revenue
Revenue
(Year) (bbl) (bbl) $/bbl MM$ MM$ MM$ MM$ MM$ MM$
0 0 0 1.00 60.0 0.0000 0.000 0.000 7
1 469658 469658 1.03 61.5 28.8840 1.444 27.440 2.41 2.80
2 269734 739392 1.05 63.0 17.0034 0.850 16.153 1.81 1.68
3 228100 967492 1.08 64.6 14.7383 0.737 14.001 1.68 1.01
4 225000 1192492 1.10 66.2 14.9015 1.863 13.039 1.68 0.60
5 215000 1407492 1.13 67.9 14.5952 1.824 12.771 1.65 0.36
6 200000 1607492 1.16 69.6 13.9163 1.740 12.177 1.60
TOTAL 1607492 10.82
Page 78
Table A2: NPV calculation for Conventional Completion (Continuation)
Total Tax Loss Taxable Income After Tax Cum. Discounted
Deduction Income Tax, MM$ NCF NCF NCF
MM$ MM$ MM$ 45% MM$ MM$ MM$
1.40 (1.40) (7.00) (7.00) (7.00)
5.21 (1.40) 20.83 9.37 15.66 8.66 13.92
3.49 12.66 5.70 8.65 17.30 6.83
2.69 11.31 5.09 7.23 24.53 5.08
2.28 10.76 4.84 6.52 31.05 4.07
2.01 10.76 4.84 6.28 37.33 3.49
1.60 10.58 4.76 5.82 43.15 2.87
Total 29.25
APPENDIX B: NPV PROBABILISTIC RESULTS FOR CASE STUDY 1
Figure B1: NPV uncertainty analysis for IWC
Page 79
Figure B2: NPV uncertainty analysis for Conventional Completion
Page 80
Figure B3: Spider Chart for both completions
APPENDIX C: NPV PROBABILISTIC RESULTS FOR CASE STUDY 2
Figure C1: NPV uncertainty analysis for IWC
40
50
60
70
80
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV - IWC
Oil price
Income Tax
Oil price Inflation
Discount rate
CAPEX
30
35
40
45
50
55
60
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV - NON IWC
Oil price
Income Tax
Oil price Inflation
Workover cost per year
Discount rate
Page 81
Figure C2: NPV uncertainty analysis for NON -IWC
Figure C3: Spider Chart for both completions
100
120
140
160
180
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV IWC
oil price
Income Tax
Oil price Inflation
Discount rate
Workover cost per year
80
100
120
140
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV - NON IWC
oil price
Income Tax
Oil price Inflation
Workover cost per year Discount rate
CAPEX
Page 82
APPENDIX D: NPV PROBABILISTIC RESULT FOR CASE STUDY 3
Figure D1: NPV uncertainty analysis for IWC
Page 83
APPENDIX E: NPV PROBABILISTIC RESULTS FOR CASE STUDY 4
For Well B-21B
Figure E1: NPV uncertainty analysis for IWC
Figure E2: NPV uncertainty analysis for NON -IWC
Page 84
Figure E3: Spider Chart for both completions
100
120
140
160
180
200
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV - IWC
Oil price
Income Tax
Oil price Inflation
Discount rate
CAPEX
80
90
100
110
120
130
140
0.9 0.7 0.5 0.3 0.1
Percentiles of the variables
NPV - NON IWC
Oil price
Income Tax
Oil price Inflation
Workover cost per year
Discount rate
