To have an acceptable accuracy for water flooding projects, proper history matching is an important tool. Capacitance resistance model (CRM) simulates water flooding performance based on two tuning parameters of time constant and connectivity. Main advantages of CRM are its simplicity and fastness; furthermore, it needs only some field-available inputs like injection and production flow rates. CRM is reliable if producers receive the injection rate signal; in other words, duration of history matching must be enough so that the rate signal of injection is sensed in producers. It is a shortcoming of CRM that the results might not be accurate as a result of short history. In the common CRM, time constant is considered to be a static parameter (constant number) during the history of simulation. However, time constant is a time-dependent function that depends on the reservoir nature. In this paper, a new model has been developed as it decreases model dependency on the history matching length by shifting time axis. This new definition adds a rate shift constant to the model mathematics. Moreover, a new model is considering dynamic time constants. This new model is called dynamic capacitance resistance model (DCRM). Two reservoir models have been simulated to analyze the performance of DCRM, and, as a result, it is found that the static time constant is an erroneous assumption. Finally, the accuracy of the results has been improved since the degree-of-freedom of the CRM increased in the new version.

References

References
1.
Ahmadi
,
Y.
,
Eshraghi
,
S. E.
,
Bahrami
,
P.
,
Hasanbeygi
,
M.
,
Kazemzadeh
,
Y.
, and
Vahedian
,
A.
,
2015
, “
Comprehensive Water-Alternating-Gas (WAG) Injection Study to Evaluate the Most Effective Method Based on Heavy Oil Recovery and Asphaltene Precipitation Tests
,”
J. Pet. Sci. Eng.
,
133
, pp.
123
129
.
2.
Kazemi
,
K.
,
Rostami
,
B.
,
Khosravi
,
M.
, and
Bejestani
,
D. Z.
,
2015
, “
Effect of Initial Water Saturation on Bypassed Oil Recovery During CO2 Injection at Different Miscibility Conditions
,”
Energy Fuels
,
29
(7), pp.
4114
4121
.
3.
Mamghaderi
,
A.
, and
Pourafshari
,
P.
,
2013
, “
Water Flooding Performance Prediction in Layered Reservoirs Using Improved Capacitance-Resistive Model
,”
J. Pet. Sci. Eng.
,
108
, pp.
107
117
.
4.
Yue
,
W.
, and
Wang
,
J. Y.
,
2015
, “
Feasibility of Waterflooding for a Carbonate Oil Field Through Whole-Field Simulation Studies
,”
ASME J. Energy Resour. Technol.
,
137
(
6
), p.
064501
.
5.
Alarcón
,
G. A.
,
Torres
,
C. F.
, and
Gómez
,
L. E.
,
2002
, “
Global Optimization of Gas Allocation to a Group of Wells in Artificial Lift Using Nonlinear Constrained Programming
,”
ASME J. Energy Resour. Technol.
,
124
(
4
), pp.
262
268
.
6.
Vicente
,
R.
,
Sarica
,
C.
, and
Ertekin
,
T.
,
2004
, “
A Numerical Model Coupling Reservoir and Horizontal Well Flow Dynamics—Applications in Well Completions, and Production Logging
,”
ASME J. Energy Resour. Technol.
,
126
(
3
), pp.
169
176
.
7.
Park
,
J.
,
Jin
,
J.
, and
Choe
,
J.
,
2016
, “
Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering
,”
ASME J. Energy Resour. Technol.
,
138
(
1
), p.
012906
.
8.
Siavashi
,
M.
,
Tehrani
,
M. R.
, and
Nakhaee
,
A.
,
2016
, “
Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function
,”
ASME J. Energy Resour. Technol.
,
138
(
5
), p.
052903
.
9.
Weber
,
D. B.
,
Edgar
,
T. F.
,
Lake
,
L. W.
,
Lasdon
,
L. S.
,
Kawas
,
S.
, and
Sayarpour
,
M.
,
2009
, “
Improvements in Capacitance-Resistive Modeling and Optimization of Large Scale Reservoirs
,”
SPE Western Regional Meeting
, San Jose, CA, Mar. 24–26,
SPE
Paper No. SPE-121299-MS.
10.
Sayyafzadeh
,
M.
,
Pourafshari
,
P.
, and
Rashidi
,
F.
,
2011
, “
A Novel Method to Model Water-Flooding Via Transfer Functions Approach
,”
SPE Project and Facilities Challenges Conference at METS
, Doha, Qatar, Feb. 13–16,
SPE
Paper No. SPE-141379-MS.
11.
Sayyafzadeh
,
M.
,
Mamghaderi
,
A.
,
Pourafshari
,
P.
, and
Haghighi
,
M.
,
2011
, “
A New Method to Forecast Reservoir Performance During Immiscible and Miscible Gas-Flooding Via Transfer Functions Approach
,”
SPE Asia Pacific Oil and Gas Conference and Exhibition
, Jakarta, Indonesia, Sept. 20–22,
SPE
Paper No. SPE-145384-MS.
12.
Sayarpour
,
M.
,
2008
, “
Development and Application of Capacitance-Resistive Models to Water/CO2 Floods
,”
Ph.D. dissertation
, The University of Texas at Austin, Austin, TX.https://www.researchgate.net/publication/280579098_Development_and_Application_of_Capacitance-Resistive_Models_to_WaterCO2_Floods
13.
Mirzayev
,
M.
,
Riazi
,
N.
,
Cronkwright
,
D.
,
Jensen
,
J. L.
, and
Pedersen
,
P. K.
,
2015
, “
Determining Well-To-Well Connectivity in Tight Reservoirs
,”
SPE/CSUR Unconventional Resources Conference
, Calgary, AB, Canada, Oct. 20–22,
SPE
Paper No. SPE-175943-MS.
14.
Champenoy
,
N. R.
, and
Fleming
,
A. E. P.
,
2013
, “
System and Method of Determining and Optimizing Waterflood Performance
,” Chevron U.S.A. Inc., San Ramon, CA, U.S. Patent No.
13/968,097
.http://www.google.com/patents/US20150051838
15.
Sayarpour
,
M.
,
Kabir
,
C. S.
,
Sepehrnoori
,
K.
, and
Lake
,
L. W.
,
2011
, “
Probabilistic History Matching With the Capacitance–Resistance Model in Waterfloods: A Precursor to Numerical Modeling
,”
J. Pet. Sci. Eng.
,
78
(1), pp.
96
108
.
16.
Izgec
,
O.
, and
Kabir
,
C. S.
,
2010
, “
Quantifying Nonuniform Aquifer Strength at Individual Wells
,”
SPEREE
,
13
(2), pp.
296
305
.
17.
Izgec
,
O.
, and
Kabir
,
C. S.
,
2010
, “
Understanding Reservoir Connectivity in Waterfloods Before Breakthrough
,”
J. Pet. Sci. Eng.
,
75
(1–2), pp.
1
12
.
18.
Cao
,
F.
,
Luo
,
H.
, and
Lake
,
L. W.
,
2014
, “
Development of a Fully Coupled Two-Phase Flow Based on Capacitance-Resistance-Model (CRM)
,”
SPE Improved Oil Recovery Symposium
, Tulsa, OK, Apr. 12–16,
SPE
Paper No. SPE-169485-MS.
19.
Kabir
,
C. S.
, and
Boundy
,
F.
,
2011
, “
Analytical Tools Aid Understanding of History-Matching Effort in a Fractured Reservoir
,”
J. Pet. Sci. Eng.
,
78
(2), pp.
274
282
.
20.
Albertoni
,
A.
,
2002
, “
Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods
,”
M.Sc. thesis
, The University of Texas at Austin,
Austin, TX
.
21.
Gentil
,
P. H.
,
2005
, “
The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients
,”
M.Sc. thesis
, The University of Texas at Austin, Austin, TX.https://www.pge.utexas.edu/images/pdfs/theses05/gentil.pdf
22.
Yousef
,
A. A.
,
2005
, “
Investigating Statistical Techniques to Infer Interwell Connectivity From Production and Injection Rate Fluctuations
,”
Ph.D. dissertation
, The University of Texas at Austin, Austin, TX.https://repositories.lib.utexas.edu/handle/2152/2456
23.
Sayarpour
,
M.
,
Zuluaga
,
E.
,
Kabir
,
C. S.
, and
Lake
,
L. W.
,
2007
, “
The Use of Capacitance-Resistive Models for Rapid Estimation of Waterflood Performance
,”
SPE Annual Technical Conference and Exhibition
, Anaheim, CA, Nov. 11–14,
SPE
Paper No. SPE-110081-MS.
24.
Lee
,
K. H.
,
Ortega
,
A.
,
Ghareloo
,
A.
, and
Ershaghi
,
I.
,
2011
, “
An Active Method for Characterization of Flow Units Between Injection/Production Wells by Injection-Rate Design
,”
SPEREE
,
14
(4), pp.
433
445
.
25.
Wang
,
H.
,
Liao
,
X.
,
Dou
,
X.
,
Shang
,
B.
,
Ye
,
H.
,
Zhao
,
D.
,
Liao
,
C.
, and
Chen
,
X.
,
2014
, “
Potential Evaluation of CO2 Sequestration and Enhanced Oil Recovery of Low Permeability Reservoir in the Junggar Basin, China
,”
Energy Fuels
,
28
(5), pp.
3281
3291
.
26.
Parekh
,
B.
, and
Kabir
,
C. S.
,
2011
, “
Improved Understanding of Reservoir Connectivity in an Evolving Waterflood With Surveillance Data
,”
SPE Annual Technical Conference and Exhibition
, Denver, CO, Oct. 30–Nov. 2,
SPE
Paper No. SPE-146637-MS.
27.
Salazar-Bustamante
,
M.
,
Gonzalez-Gomez
,
H.
,
Matringe
,
S.
, and
Castineira
,
D.
,
2012
, “
Combining Decline-Curve Analysis and Capacitance/Resistance Models to Understand and Predict the Behavior of a Mature Naturally Fractured Carbonate Reservoir Under Gas Injection
,”
SPE Latin America and Caribbean Petroleum Engineering Conference
, Mexico City, Mexico, Apr. 16–18,
SPE
Paper No. SPE-153252-MS.
28.
Nguyen
,
A. P.
,
2012
, “
Capacitance Resistance Modeling for Primary Recovery, Waterflood, and Water-CO2 Flood
,” Ph.D. dissertation, The University of Texas at Austin, Austin, TX.
29.
Can
,
B.
, and
Kabir
,
C. S.
,
2012
, “
Simple Tools for Forecasting Waterflood Performance
,”
SPE Annual Technical Conference and Exhibition
, San Antonio, TX, Oct. 8–10,
SPE
Paper No. SPE-156956-MS.
30.
Tafti
,
A.
,
Ershaghi
,
T.
,
Rezapour
,
I.
, and
Ortega
,
A.
,
2013
, “
Injection Scheduling Design for Reduced Order Waterflood Modeling
,”
SPE Western Regional and AAPG Pacific Section Meeting
, Joint Technical Conference, Monterey, CA, Apr. 19–25,
SPE
Paper No. SPE-165355-MS.
31.
Soroush
,
M.
,
Kavuani
,
D.
, and
Jensen
,
J. L.
,
2014
, “
Interwell Connectivity Evaluation in Cases of Changing Skin and Frequent Production Interruptions
,”
J. Pet. Sci. Eng.
,
122
, pp.
616
630
.
32.
Tao
,
Q.
, and
Bryant
,
S. L.
,
2015
, “
Optimizing Carbon Sequestration With the Capacitance/Resistance Model
,”
SPE J.
,
20
(5), pp.
1094
1102
.
33.
Eshraghi
,
S. E.
,
Rasaei
,
M. R.
, and
Zendehboudi
,
S.
,
2016
, “
Optimization of Miscible CO2 EOR and Storage Using Heuristic Methods Combined With Capacitance/Resistance and Gentil Fractional Flow Models
,”
J. Nat. Gas Sci. Eng.
,
32
, pp.
304
318
.
34.
Eshraghi
,
S. E.
,
Rasaei
,
M. R.
,
Pourafshary
,
P.
, and
Masoumi
,
A. S.
,
2016
, “
Reservoir Heterogeneity Characterization by Capacitance-Resistance Model in Water-Flooding Projects
,”
Iran. J. Oil Gas Sci. Technol.
,
5
(3), pp.
1
13
.
35.
Kim
,
J. S.
,
2011
, “
Development of Linear Capacitance-Resistance Models for Characterizing Waterflooded Reservoirs
,”
M.Sc. thesis
, The University of Texas at Austin, Austin, TX.https://repositories.lib.utexas.edu/handle/2152/ETD-UT-2011-12-4644
36.
Mamghaderi
,
A.
,
Bastami
,
A.
, and
Pourafshary
,
P.
,
2013
, “
Optimization of Waterflooding Performance in a Layered Reservoir Using a Combination of Capacitance-Resistive Model and Genetic Algorithm Method
,”
ASME J. Energy Resour. Technol.
,
135
(1), p. 013102.
37.
Jafroodi
,
N.
, and
Zhang
,
D.
,
2011
, “
New Method for Reservoir Characterization and Optimization Using CRM–EnOpt Approach
,”
J. Pet. Sci. Eng.
,
77
(2), pp.
155
171
.
38.
Moreno
,
G. A.
,
2013
, “
Multilayer Capacitance–Resistance Model With Dynamic Connectivities
,”
J. Pet. Sci. Eng.
,
109
, pp.
298
307
.
39.
Moreno
,
G. A.
, and
Lake
,
L. W.
,
2014
, “
Input Signal Design to Estimate Interwell Connectivities in Mature Fields From the Capacitance Resistance Model
,”
Pet. Sci.
,
11
(4), pp.
563
568
.
40.
Laochamroonvorap
,
R.
,
2013
, “
Advances in the Development and Application of a Capacitance-Resistance Model
,”
M.Sc. thesis
, The University of Texas at Austin, Austin, TX.https://repositories.lib.utexas.edu/handle/2152/22372
41.
Kaviani
,
D.
,
Sorooush
,
M.
, and
Jensen
,
J. L.
,
2014
, “
How Accurate Are Capacitance Model Connectivity Estimates?
,”
J. Pet. Sci. Eng.
,
122
, pp.
439
452
.
42.
Zhang
,
Z.
,
Li
,
H.
, and
Zhang
,
D.
,
2015
, “
Water Flooding Performance Prediction by Multi-Layer Capacitance-Resistive Models Combined With the Ensemble Kalman Filter
,”
J. Pet. Sci. Eng.
,
127
, pp.
1
19
.
43.
Chitsiripanich
,
S.
,
2015
, “
Field Application of Capacitance-Resistance Models to Identify Potential Location for Infill Drilling
,”
M.Sc. thesis
, The University of Texas at Austin, Austin, TX.https://repositories.lib.utexas.edu/handle/2152/32042
44.
Kaviani
,
D.
,
Jensen
,
J. L.
, and
Lake
,
L. W.
,
2012
, “
Estimation of Interwell Connectivity in the Case of Unmeasured Fluctuating Bottomhole Pressures
,”
J. Pet. Sci. Eng.
,
90–91
, pp.
79
95
.
45.
Sayarpour
,
M.
,
Zuluaga
,
E.
,
Kabir
,
C. S.
, and
Lake
,
L. W.
,
2009
, “
The Use of Capacitance–Resistance Models for Rapid Estimation of Waterflood Performance and Optimization
,”
J. Pet. Sci. Eng.
,
69
(3–4), pp.
227
238
.
You do not currently have access to this content.