Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. In many applications in reservoir modeling and management, there is a need for rapid estimation of large-scale reservoirs. The capacitance-resistive model (CRM), regarded as a promising rapid evaluator of reservoir performance, has recently been used for simulation of single-layer reservoirs. Injection and production rates are considered as input and output signals in this model. Connections between the wells and the effects of injection rates on production rates are calculated based on these signals to develop a simple model for the reservoir. In this study, CRM is improved to model a multilayer reservoir and is applied to estimate and optimize waterflooding performance in an Iranian layered reservoir. In this regard, CRM is coupled with production logging tools (PLT) data to study the effects of layers. A fractional-flow model is also coupled with the developed CRM to estimate oil production. Genetic algorithm (GA) method is used to minimize the error objective function for the total production history and oil production history to evaluate model parameters. GA is then used to maximize oil production by reallocating the injected water volumes, which is the main purpose of this research. The results show that our fast method is able to model liquid and oil production history and is in good agreement with available field data. Taking into account the reservoir constraints, the optimal injection schemes have been obtained. For the proposed injection profile, the field hydrocarbon production will increase by up to 1.8% until 2016. Also, the wells will reach the water-cut constraint 2 yr later than the current situation, which increases the production period of the field.

References

References
1.
Kosmidis
,
V. D.
,
Perkins
,
J. D.
, and
Pistikopoulos
,
E. N.
,
2004
, “
Optimization of Well Oil Rate Allocations in Petroleum Fields
,”
Ind. Eng. Chem. Res.
,
43
, pp.
3513
3527
.10.1021/ie034171z
2.
Wang
,
P.
,
Litvak
,
M.
, and
Aziz
,
K.
,
2002
, “
Optimization of Production Operations in Petroleum Fields
,”
SPE
Annual Technical Conference and Exhibition
.10.2118/77658-MS
3.
Liang
,
X.
,
Weber
,
D. B.
,
Edgar
,
T. F.
,
Lake
,
L. W.
,
Sayarpour
,
M.
, and
Al-Yousef
,
A.
,
2007
, “
Optimization of Oil Production Based on a Capacitance Model of Production and Injection Rates
,”
SPE
Hydrocarbon Economics and Evaluation Symposium
,
Dallas, TX
, Paper No. 107713-MS.10.2118/107713-MS
4.
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
, Paper No. 110081.10.2118/110081-MS
5.
Albertoni
,
A.
, and
Lake
,
L. W.
,
2003
, “
Inferring Interwell Connectivity Only From Well-Rate Fluctuations in Waterfloods
,”
J. SPE Reservoir Eval. Eng.
,
6
(
1
), pp.
6
16
.10.2118/83381-PA
6.
Gentil
,
P. H.
,
2005
, “
The Use of Multilinear Regression Models in Patterned Waterfloods: Physical Meaning of the Regression Coefficients
,” M.S. thesis,
The University of Texas at Austin
, Austin, TX.
7.
Yousef
,
A. A.
,
Gentil
,
P. H.
,
Jensen
,
J. L.
, and
Lake
,
L. W.
,
2006
, “
A Capacitance Model to Infer Interwell Connectivity From Production- and Injection-Rate Fluctuations
,”
J. SPE Reservoir Eval. Eng.
,
9
(
6
), pp.
630
646
.10.2118/95322-PA
8.
Yousef
,
A. A.
,
Jensen
,
J. L.
, and
Lake
,
L. W.
,
2006
, “
Analysis and Interpretation of Interwell Connectivity From Production and Injection Rate Fluctuations Using a Capacitance Model
,”
SPE
/DOE Symposium on Improved Oil Recovery,
Tulsa, OK
, Paper No. 99998-MS.10.2118/99998-MS
9.
Sayarpour
,
M.
,
2008
, “
Development and Application of Capacitance-Resistive Models to Water/Co2 Floods
,” Ph.D. thesis, The University of Texas at Austin, Austin, TX.
10.
Delshad
,
M.
,
Bastami
,
A.
, and
Pourafshary
,
P.
,
2009
, “
The Use of Capacitance-Resistive Model for Estimation of Fracture Distribution in the Hydrocarbon Reservoir
,”
SPE
Technical Symposium and Exhibition,
Alkhobar, Saudi Arabia
, Paper No. 126076-MS.10.2118/126076-MS
11.
Goldberg
,
D. E.
,
1989
,
Genetic Algorithm in Search Optimization and Machine Learning
,
1st ed.
,
Addison-Wesley
,
Boston
.
12.
Kalyanmoy
,
D.
,
2004
,
Optimization for Engineering Design Algorithms and Examples
,
Prentice-Hall
,
India
.
13.
Guyaguler
,
B.
, and
Horne
,
R.
,
2000
, “
Optimization of Well Placement
,”
ASME J. Energy Resour. Technol.
,
122
(
2
), pp.
64
70
.10.1115/1.483164
14.
Demirkaya
,
G.
,
Besarati
,
S.
,
Padilla
,
R. V.
,
Archibold
,
A. R.
,
Goswami
,
D. Y.
,
Rahman
,
M. M.
, and
Stefanakos
,
E. L.
,
2012
, “
Multi-Objective Optimization of a Combined Power and Cooling Cycle for Low-Grade and Midgrade Heat Sources
,”
ASME J. Energy Resour. Technol.
,
134
(
3
), p.
032002
.10.1115/1.4005922
15.
Alarcon
,
G. A.
,
Torres
,
C. F.
, and
Gomez
,
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
.10.1115/1.1488172
16.
Penny
,
G.
,
Pursley
,
J. T.
, and
Holcomb
,
D.
,
2005
, “
Microemulsion Additives Enable Optimized Formation Damage Repair and Prevention
,”
ASME J. Energy Resour. Technol.
,
127
(
3
), pp.
233
239
.10.1115/1.1937419
17.
Rahman
,
M. M.
, and
Rahman
,
M. K.
,
2012
,
“Optimizing Hydraulic Fracture to Manage Sand Production by Predicting Critical Drawdown Pressure in Gas Well,”
ASME J. Energy Resour. Technol.
,
134
(
1
), p.
013101
.10.1115/1.4005239
18.
Mehdizadeh
,
P.
, and
Perry
,
D. T.
,
2004
, “
The Role of Well Testing in Recognizing Deferred Production Revenue
,”
ASME J. Energy Resour. Technol.
,
126
(
3
), pp.
177
183
.10.1115/1.1789521
You do not currently have access to this content.