Reservoir Characterization From Production and Injections Fluctuations

8/12/2019 Reservoir Characterization From Production and Injections Fluctuations

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Reservoir Characterization

From Production andInjection Fluctuations

Larry W. Lake

The University of Texas at [email protected]


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Outline

Introduction

The Model Applications of the Model

Synthetic Fields

(Synfields)

Field Applications

Uses of the Model

Validation


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Prior and Current Work

Belkis Refunjol

Jorge SAntana Pizarro

(Petrobras)

Isolda Griffiths (Shell)

Alejandro Albertoni (Nexen)

Pablo Gentil (ENI)

Ali Al-Yousif (Aramco)

Danial Kaviani (TAMU)

Thang Bui (TAMU)

Xming Liang

Morteza Sayarpour (Chevron)

Sami Kaswas (Exxon)

Tom Edgar, ChE

Leon Lasdon, IROM

Jerry Jensen (U.Calgary)

Alireza Mollaei, PGE Ahn Phoung Nguyen, ChE

Fei Cao, PGE

Jacob McGregor, PGE

Jong Suk Kim, ChE

Wenle Wang, PGE

PastPresent


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What others say about modeling

Bratvold and BickelTwo types

Verisimilitude- the appearance of reality

Cogent- enables decisions Haldorsen.the progress of ideas

Youth= simple, nave

Adolescence=complex, naveMiddle age=complex, sophisticated

Maturity= simple, sophisticated


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Hypothesis

Characteristics of a reservoir can be

inferred from analyzing productionand injection data only


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Boundary Conditions

Must be injection project

Rates are most abundant data type

Rates must vary

No geologic model required

Everything done in a spreadsheet


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Outline

Introduction

The Model Applications of the Model

Synthetic Fields

(Synfields)

Field Applications

Uses of the Model

Validation


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q(t)= q(t0

)e ( t t0 ) + I(t) 1 e (

t t0 )

ctVp pwf,t pwf,0t t0 1 e (

t t0 )

CRM Continuity Equation

ctVpdp

dt= i(t) q(t)

dq(t)

dt+ 1 q(t)= 1 i(t) J dpwfdt

ctVp

J

Ordinary Differential Equation:

Continuity:

Solution:

q(t)i(t)

BHPInjectionPrimary

q(t)= J p pwfProduction Rate:
http://c/Users/gmurrell/AppData/Local/Microsoft/Windows/Documents%20and%20Settings/mueu/Local%20Settings/Presentations/DEC%2011%202006/CRM%20Tank.xls


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Signal Response

Production response to an injection signal

Connectivity

ij = 1 day

fij = 0%

Connectivity

ij = 1 day

fij = 100%

Connectivity

ij = 6 days

fij = 100%

Connectivity

ij = 6 daysfij = 65%


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Capacitance-Resistance Model (CRMT)

( ) k

tt

kk Ieeqq

+=

11

q(t)I(t)

J

Vc pt=

Time constant


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f2j

f6j

f4j

f3j

f5j

jf1j

f11f12

f13

I6

I1I2

I3

I4I5

qj(t)

Capacitance-Resistance Model (CRMP)

( ) ik

n

iij

tt

kjjk Ifeeqq

i

jj

=

+= 11 1

j

pt

jJ

Vc

=

11

=

pn

j

ijf

Time constant

Inter-well connectivity or gain

Drainage volume

around a producer


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Capacitance-Resistance Model (CRMIP)

Ii(t)

qj(t)

fij

ij

ij

pt

ijJ

Vc

=

11

=

pn

j

ijf

Time constant

Inter-well connectivity or gain

( )=

+=

i

ijij

n

i

ikij

tt

kijjk Ifeeqq1

1 1
http://hou150nt3usr16.hou150.chevrontexaco.net/mueu$/Presentation/fij_fji%20Fractions.xls


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Steady-State Connectivity Map

Producer

Water Injector

Carbon Dioxide Injector 0 1,000 ft

Better CO2

Performance


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Interwell Connectivity

Two Equally Viable Solutions

C


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Transient Interwell Connectivity

After 10 days

T i I ll C i i


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After 30 days

T i t I t ll C ti it


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After 90 days



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After 180 days



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After 365 days



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After 2 years



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After 4 years



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4 years


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Gains >0.5

Mature West Texas Waterflood

Injector

Producer

Gains>0.5


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Gains >0.4


Injector

Producer


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Gains >0.3


Gains>0.3Injector

Producer


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Gains >0.2


Gains>0.2Injector

Producer


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Time Constants

Reservoir A


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Producer 184 Good Fit

R2 = 0.961

err = 0.146Bbl/

day

Month


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Producer 127 Good Fit

R2 = 0.696

err = 0.037

outliers

Bbl/

day

Month


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Producer 74 Poor Fit

R2 = -1.03

err = 0.143

Bbl/

day

Month


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Producer 201 Poor Fit

R2 = 0.793

err = 6.58

Bbl/

day

Month


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CRM: Oil Fractional-Flow Model

fo(t)= qoqo+ qw =1

1+ WOR(t)

qo(t)= fo(t)q(t)

fo (t)= 11+ a CWI(t)b

log 1

fo (t) 1

= loga + blog CWI(t)


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Outline

Introduction

The Model

Applications of the Model

Synthetic Fields

(Synfields)Field Applications

Uses of the Model

Validation


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Future Injection

Historic Period 131 Active Injectors

Prediction Period 97 Active Injectors

Injection has been concentrated in fewer wells (37injectors shut-in)

27.3% of historic field injection from injectors shut-

in throughout prediction period

O ti l I j ti d P di t d Oil P d ti f


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Optimal Injection and Predicted Oil Production for

the Field

0 20 40 60 80 100 120 140 160 180 2002

3

4

5

6x 10

4

Month

b

bl/day

Historic

Optimal

0 20 40 60 80 100 120 140 160 180 200500

1000

1500

2000

2500

3000

Month

bbl/da

y

Historic Oil Production

Predicted Oil Production

Extrapolated Oil Production


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Injection Shares

Injector Number

Percent of

Total


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Production Shares

P112 P195

Producer Number

Percent of

Total


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GardnerHypeCurve

TheGardnerGroup40Jim Honefenger (P.E. Moseley & Associates, Inc.)


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Outline

Introduction

The Model

Applications of the Model

Synthetic Fields

(Synfields)Field Applications

Uses of the Model

Validation


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Validation

Just how do we scientifically validate

geoscience hypotheses?

Remember:

Characteristics of a reservoir can be inferredfrom analyzing production and injection data

only

Recognizing testable hypotheses can be subtle and


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g g yp

requires practice. To do it, ask how would one test this

hypothesis.

If the duck is lighter than this woman, then she is

a witch.


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Synfield Cases

Heterogeneity

Large compressibility

Fractures

Barriers

Anisotropy

Partial completions

Large shut in times Changing BHP

All agree with imposed geology


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Validation

Field Injectant Independent Data AgreeWith Data

Synfields Water Simulation Very well

Characteristics of a reservoir can be inferred from

analyzing production and injection data only


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Retrodiction


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Validation



Synfields Water Retrodiction Very well




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Chihuido Field

Good correlation

Inferred faults are in yellow

Gains and time constants

reproduce known geological features


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Validation




Chuido Water Faults from seismic Reasonably



SWCF Flow Capacity


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SWCF Flow Capacity

7516

7519

7523

7524

From Al-Yousef (2006)

Homogeneous

V lid ti


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Validation





SWCFU Water Anecdotal fractures Reasonably




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V lid ti


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Validation






NSF II Water Structure Well



N th B k D C i


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North Buck Draw Comparison

CM correlates with tracer breakthrough time

0

5

10

15

20

300 5 10 15 20 25 35Tracer Breakthrough Time (months)

Spearmano

rCMT

ime

(months)

SpearmanCM

Linear (CM)

V lid ti


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Validation



Snyfields Water Retrodiction Very well



NSF II Water Structure Well

NBDU Gas Tracer data Fairly well




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Williston Basin Field

V lid ti


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Validation

Field Injectant Independent Data Agree

With Data


Snyfields Water Retrodiction Very well



NSF I Water Structure Well

NBDU Gas Tracer data Fairly well

Will. Basin Water Acoustic impedance Reasonably



F t W k


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Future Work

Working spreadsheet

Couple to GAMS

Excel vs. MATLABMultiplotting (visualization)

Integrate with DA/VOI approaches

Propagating error/uncertainty

More validation (oil in tank)

Extend to primary recovery Fluid allocation studies (conformance)

Optimize to produce more oil

Add EOR model(s)


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Model Fit and Prediction Algorithm


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Remove outliers

Maximize NPV of future oil recovery

Warm start

Gainfit

Remove

inactive wellsRemove gains

based on distance

Remove small

gains

Gainfit #2Calculate residuals

and replace outliersGainfit #3

Gainfit #1

Fracfit #1Calculate residuals

and remove outliersFracfit #2

Reservoir

model


~2.5 hrscomputation

time



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Remove outliers


Warm start

Gainfit

Remove


based on distance

Remove small

gains



Gainfit #1



Reservoir

model



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Remove outliers


Warm start

Gainfit

Remove


based on distance

Remove small

gains



Gainfit #1



Reservoir

model



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Appraisal andConceptual

AnalysisGATE GATE

Evaluate

AlternativesGATE

DefineSelected

Alternative

GATEExecute Operate

Inevitable

Dis-appointment

PortfolioOptimization

Uncertainty

Updating

Concept Selection & Development Optimization

Real Options

Portfolio Management and

Project Selection

Addressing Risks Throughout the E&P Asset Lifecycle

VOI; Impact

of Estimates& Methods

Financial Risk

Management

Cost and Schedule Estimating; Execution Risk Management

HSE Risk Management

Real-Time Optimization

and Risk Management

Valuing Price

Forecasts

Capital

Allocation w/

Uncertain

Arrivals

FUTURE:Life Cycle

Assessments

Contracting

Strategies

(lump sum vcost plus?)

MPD &

Blowouts;

Drlg Safety;

OffshoreSpills

Simple Model

Development


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Producer 210 (large distance)


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Producer 210 (large distance)

093.0882.0R

2

==

err

Bbl/

day

Producer 103 (skipped over)


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Producer 103 (skipped over)

110.0

635.0R 2

=

=

errBbl/

day

Lost Injection


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Injector Number

Lost Injection

1 f

ijj1

Np

CRM Fit Total Field


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CRM Fit Total Field

R2 = 0.956Bbl/

day

Month