Consideration of Voidage-Replacement Ratio in Well-Placement Optimization
- Abeeb A. Awotunde (King Fahd University of Petroleum & Minerals) | Najmudeen Sibaweihi (King Fahd University of Petroleum & Minerals)
- Document ID
- Society of Petroleum Engineers
- SPE Economics & Management
- Publication Date
- January 2014
- Document Type
- Journal Paper
- 40 - 54
- 2014.Society of Petroleum Engineers
- 5.4.1 Waterflooding
- Voidage replacement ratio, multiobjective optimization, environmental impact , Well placement optimization, net present value
- 4 in the last 30 days
- 504 since 2007
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Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, the voidage-replacement ratio (VRR) and net present value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field-development activities on the environment, whereas NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well-placement optimization has been performed mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of unity (or close to unity) during waterflooding. A multiobjective approach incorporating NPV and VRR is proposed for solving the well-placement-optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase had the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights was used in the third phase to describe the relative importance of the NPV and the VRR, and a comparison of how these weights affect the optimized NPV and VRR values is provided. We applied the method to determine the optimum placement of wells to three sample reservoirs: the first with a distributed permeability field, the second being a channel reservoir with four facies, and the third being a slightly heterogeneous reservoir. Two evolutionary-type algorithms—the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE)—were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary-oil-recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of unity.
|File Size||1 MB||Number of Pages||15|
Artus, V., Durlofsky, L. J., Onwunalu, J., et al. 2006. Optimization of Nonconventional Wells under Uncertainty using Statiscal Proxies. Computat. Geosci. 10 (4): 389–404. http://dx.doi.org/10.1007/s10596-006-9031-9.
Auger, A. and Hansen, N. 2011a. Tutorial: CMA-ES – Evolutionary Strategies and Covariance Matrix Adaptation. Oral presentation given at GECCO ’11, Dublin, Ireland, 12–16 July.
Auger, A. and Hansen, N. 2011b. Theory of Evolution Strategies: A New Perspective. In Theory of Randomized Search Hueristics: Foundations and Recent Developments, eds. A. Auger and B. Doerr, 289–325. Hackensack, New Jersey: World Scientific Publishing.
Bouzarkouna, Z., Ding, D. and Auger, A. 2010. Using Evolution Strategy with Meta-models for Well Placement Optimization. Oral presentation given at ECMOR XII – 12th European Conference on the Mathematics of Oil Recovery, Oxford, UK, 6–9 September.
Bouzarkouna, Z., Ding, D. Y. and Auger, A. 2011. Partially Separated Meta-models with Evolution Strategies for Well Placement Optimization. Paper SPE 143292 presented at the SPE EUROPEC/EAGE Annual Conference and Exhibition, Vienna, Austria, 23–26 May. http://dx.doi.org/10.2118/143292-MS.
Clark, R. A. Jr. and Ludolph, B. 2003. Voidage Replacement Ratio Calculation in Retrograde Condensate to Volatile Oil Reservoirs Undergoing EOR Processes. Paper SPE 84359 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 5–8 October. http://dx.doi.org/10.2118/84359-MS.
Deb, K. 2001. Multi-Objective Optimization using Evolutionary Algorithms. West Sussex, England: John Wiley & Sons, Ltd.
Farshi, M. M. 2008. Improving Genetic Algorithms for Optimum Well Placement. MS thesis, Stanford University, Stanford, California (June 2008).
Guyaguler, B. and Horne, R. N. 2004. Uncertainty Assessment of Well-Placement Optimization. SPE Res Eval & Eng 7 (1): 24–32. http://dx.doi.org/10.2118/87663-PA.
Hansen, N. and Ostermeier, A. 2001. Completely Derandomized Self-Adaptation in Evolution Strategies. Evol. Comput. 9 (2): 159–195. http://dx.doi.org/10.1162/106365601750190398.
Hansen, N. and Ostermeier, A. 2013. Principled Design of Continuous Stochastic Search: From Theory to Practice. In Theory and Principled Methods for the Design of Metaheuristics, eds. Y. Borenstein and A. Moraglio. New York City, New York: Springer.
Khan, M. Y. 1993. Theory and Problems in Financial Management. Boston, Massachusetts: McGraw Hill Higher Education.
Onwunalu, J. E. and Durlofsky, L. 2011. A New Well-Pattern Optimization Procedure for Large-Scale Field Development. SPE J. 16 (3): 594–607. http://dx.doi.org/10.2118/124364-PA.
Onwunalu, J. and Durlofsky, L. 2010. Application of a Particle Swarm Optimization Algorithm for Determining Optimum Well Location and Type. Computat. Geosci. 14 (1): 183–198. http://dx.doi.org/10.1007/s10596-009-9142-1.
Price, K., Storn, R. M. and Lampinen, J. A. 2005. Differential Evolution: A Practical Approach to Global Optimization. New York City, New York: Springer-Verlag.
Storn, R. M. and Price, K. 1996. Minimizing the Real Functions of the ICEC’96 Contest by Differential Evolution. In Evolutionary Computation, 1996: Proceedings of IEEE International Conference on Evolutionary Computation, 842–844.