0
Petroleum Wells-Drilling/Production/Construction

Optimization of Waterflooding Performance in a Layered Reservoir Using a Combination of Capacitance-Resistive Model and Genetic Algorithm Method

[+] Author and Article Information
Azadeh Mamghaderi

e-mail: mamghaderi@ut.ac.ir

Alireza Bastami

e-mail: bastami@ut.ac.ir

Peyman Pourafshary

e-mail: pourafshari@ut.ac.ir
Institute of Petroleum Engineering,
University of Tehran,
Tehran, Iran

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received March 19, 2012; final manuscript received August 11, 2012; published online November 28, 2012. Assoc. Editor: Jonggeun Choe.

J. Energy Resour. Technol 135(1), 013102 (Nov 28, 2012) (9 pages) Paper No: JERT-12-1056; doi: 10.1115/1.4007767 History: Received March 19, 2012; Revised August 11, 2012

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.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

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. [CrossRef]
Wang, P., Litvak, M., and Aziz, K., 2002, “Optimization of Production Operations in Petroleum Fields,” SPE Annual Technical Conference and Exhibition. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
Goldberg, D. E., 1989, Genetic Algorithm in Search Optimization and Machine Learning, 1st ed., Addison-Wesley, Boston.
Kalyanmoy, D., 2004, Optimization for Engineering Design Algorithms and Examples, Prentice-Hall, India.
Guyaguler, B., and Horne, R., 2000, “Optimization of Well Placement,” ASME J. Energy Resour. Technol., 122(2), pp. 64–70. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Drainage area of an NL-layer reservoir

Grahic Jump Location
Fig. 2

Locations of wells in an Iranian layered reservoir

Grahic Jump Location
Fig. 3

Water injection profiles for injector wells

Grahic Jump Location
Fig. 4

Model parameters through the field

Grahic Jump Location
Fig. 5

Fitness value (f1) of the producer well 2-P in layer 1

Grahic Jump Location
Fig. 6

Pareto plot for the fitness values (f1) of well 2-P in layer 1 and well 1-P in layer 2

Grahic Jump Location
Fig. 7

Liquid production (well 1-P), layers 1 and 2

Grahic Jump Location
Fig. 8

Liquid production (well 2-P), layers 1 and 2

Grahic Jump Location
Fig. 9

Liquid production (well 3-P), layers 1 and 2

Grahic Jump Location
Fig. 10

Liquid production (well 4-P), layers 1 and 2

Grahic Jump Location
Fig. 11

Fitness value (f2) of the producer well 2-P in layer 1

Grahic Jump Location
Fig. 12

Oil production (well 1-P), layers 1 and 2

Grahic Jump Location
Fig. 13

Oil production (well 2-P), layers 1 and 2

Grahic Jump Location
Fig. 14

Oil production (well 3-P), layers 1 and 2

Grahic Jump Location
Fig. 15

Oil production (well 4-P), layers 1 and 2

Grahic Jump Location
Fig. 16

Pareto plot for the specified fitness values (f3 ′) of well 2-P in layer 1 and well 1-P in layer 2

Grahic Jump Location
Fig. 17

Total oil produced for the optimization scenario in comparison with the base case scenario

Grahic Jump Location
Fig. 18

Well oil production rate: (a) base case scenario and (b) optimization result scenario

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In