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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.xls8/12/2019 Reservoir Characterization From Production and Injections Fluctuations
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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.xls8/12/2019 Reservoir Characterization From Production and Injections Fluctuations
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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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Transient Interwell Connectivity
After 30 days
T i t I t ll C ti it
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Transient Interwell Connectivity
After 90 days
T i t I t ll C ti it
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Transient Interwell Connectivity
After 180 days
T i t I t ll C ti it
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Transient Interwell Connectivity
After 365 days
T i t I t ll C ti it
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Transient Interwell Connectivity
After 2 years
T i t I t ll C ti it
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Transient Interwell Connectivity
After 4 years
T i t I t ll C ti it
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Transient Interwell Connectivity
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
Mature West Texas Waterflood
Injector
Producer
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Gains >0.3
Mature West Texas Waterflood
Gains>0.3Injector
Producer
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Gains >0.2
Mature West Texas Waterflood
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
Field Injectant Independent Data AgreeWith Data
Synfields Water Simulation Very well
Synfields Water Retrodiction Very well
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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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
Field Injectant Independent Data AgreeWith Data
Synfields Water Simulation Very well
Synfields Water Retrodiction Very well
Chuido Water Faults from seismic Reasonably
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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
Field Injectant Independent Data AgreeWith Data
Synfields Water Simulation Very well
Synfields Water Retrodiction Very well
Chuido Water Faults from seismic Reasonably
SWCFU Water Anecdotal fractures Reasonably
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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V lid ti
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Validation
Field Injectant Independent Data AgreeWith Data
Synfields Water Simulation Very well
Synfields Water Retrodiction Very well
Chuido Water Faults from seismic Reasonably
SWCFU Water Anecdotal fractures Reasonably
NSF II Water Structure Well
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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
Field Injectant Independent Data AgreeWith Data
Synfields Water Simulation Very well
Snyfields Water Retrodiction Very well
Chuido Water Faults from seismic Reasonably
SWCFU Water Anecdotal fractures Reasonably
NSF II Water Structure Well
NBDU Gas Tracer data Fairly well
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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Williston Basin Field
V lid ti
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Validation
Field Injectant Independent Data Agree
With Data
Synfields Water Simulation Very well
Snyfields Water Retrodiction Very well
Chuido Water Faults from seismic Reasonably
SWCFU Water Anecdotal fractures Reasonably
NSF I Water Structure Well
NBDU Gas Tracer data Fairly well
Will. Basin Water Acoustic impedance Reasonably
Characteristics of a reservoir can be inferred from
analyzing production and injection data only
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
Model Fit and Prediction Algorithm
~2.5 hrscomputation
time
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
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
Model Fit and Prediction Algorithm
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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