Optimal well placement targeting sweet spots within the reservoir is critical for oil field development. However, geological uncertainty can potentially impact the robustness of the well planning solutions and may negatively impact field development strategies. Traditional workflows may seek the optimal well locations either on one geological model or in an average manner over a set of selected geological realizations by optimizing a chosen objective function such as Net Present Value (NPV). These approaches however tend to be somewhat deficient for realistic field case studies. Firstly, traditional workflows avoid an explicit treatment of well planning variance due to geological uncertainty. Secondly, traditional optimization normally cannot meet the requirement of optimizing two or more conflicting objective functions simultaneously.

In this paper, we propose a workflow that handles geological uncertainty in a novel manner. The workflow is based on a multi-objective optimization approach using the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The mean NPV is maximized while the standard deviation is minimized simultaneously for all well placement scenarios over all geological realizations. The power and utility of the proposed workflow is demonstrated on a reservoir case study. The results indicate that the proposed approach leads to improved decision making capabilities by providing a suite of well planning solutions that can be incorporated in decision making. Moreover, this workflow demonstrates a novel treatment of geological uncertainty. It takes into account the risk attitude of decision-makers and broadens field development strategies intelligently.


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  1. Bouzarkouna, Z.
    [2012] Well Placement Optimization. Ph.D. thesis, University of Paris-Sud, Paris, France.
    [Google Scholar]
  2. Christie, M., Eydinov, D., Demyanov, V., Talbot, J., Arnold, D. and Shelkov, V.
    [2013] Use of multi-objective algorithms in history matching of a real field, SPE 163580. SPE Reservoir Simulation Symposium, The Woodlands, Texas USA, 18–20 February2013.
    [Google Scholar]
  3. Deb, K. and Agrawal, R.B.
    [1995] Simulated binary crossover for continuous search space. Complex Systems, 9, 148–.
    [Google Scholar]
  4. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T.
    [2002] A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6, 197–.
    [Google Scholar]
  5. Emmerich, M., Beume, N. and Naujoks, B.
    [2005] An emo algorithm using the hypervolume measure as selection criterion. 2005 International Conference, Springer, 62–76.
    [Google Scholar]
  6. Ferraro, P. and Verga, F.
    [2009] Use of evolutionary algorithms in single and multi-objective optimization techniques for assisted history matching. Proc. of the Offshore Mediterranean Conference and Exhibition in Ravenna, Italy, March 25–27, 2009.
    [Google Scholar]
  7. Ferreira, J., Fonseca, C. and Gaspar-Cunha, A.
    [2007] Methodology to Select Solutions from the Pareto-Optimal Set: A Comparative Study. 2007 Genetic and Evolutionary Computation Conference (GECCO’2007),ACM Press, London, UK, vol. 1, 789–796.
    [Google Scholar]
  8. Guyaguler, B. and Horne, R.
    [2000] Optimization of well placement. Journal of Energy Resources Technology, 122, 70–.
    [Google Scholar]
  9. Han-Young Park, A.D.G. and King, M.J.
    [2013] Handling confuting multiple objectives using pareto-based evolutionary algorithm during history matching of reservoir performance (SPE 163623). SPE Reservoir Simulation Symposium, The Woodlands, Texas USA, February 2013.
    [Google Scholar]
  10. Isebor, O.J. and Durlofsky, L.J.
    [2014] Biobjective optimization for general oil field development. Journal of Petroleum Science and Engineering.
    [Google Scholar]
  11. Ishibuchi, H., Tsukamoto, N. and Nojima, Y.
    [2008] Evolutionary many-objective optimization: A short review. IEEE Congress on Evolutionary Computation, IEEE, 2419–2426.
    [Google Scholar]
  12. Leeuwenburgh, O., Egberts, P.J.P. and Abbink, O.A.
    [2010] Ensemble methods for reservoir life-cycle optimization and well placement (SPE 136916). Proceedings of SPE/DGS Annual Technical and Symposium and Exhibition, 04–07 April2010, Al-Khobar, Saudi Arabia.
    [Google Scholar]
  13. Marler, R. and Arora, J.
    [2004] Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization April 2004, 26(6), 369–395.
    [Google Scholar]
  14. Onwunalu, J. and Durlofsky, L.
    [2009] Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 2010(14), 183–198, doi:10.1007/s10596‑009‑9142‑1.
    https://doi.org/10.1007/s10596-009-9142-1 [Google Scholar]
  15. Raghuwanshi, M.M. and Kakde, O.G.
    [2004] Survey on multiobjective evolutionary and real coded genetic algorithms. Proceedings of the 8th Asia PaciïňĄc Symposium on Intelligent and Evolutionasy Systems, 150–161.
    [Google Scholar]
  16. Sarma, P., Durlofsky, L.J. and Aziz, K.
    [2008] Kernel principal component analysis for efficient, differentiable parameterization of multipoint geostatistics. Mathematical Geosciences, 40(1), 3–32, doi:10.1007/s11004‑007‑9131‑7.
    https://doi.org/10.1007/s11004-007-9131-7 [Google Scholar]
  17. Seshadri, A.
    [2006] A fast elitist multiobjective genetic algorithm: Nsga-ii. Class project, Oklahoma State University.
    [Google Scholar]
  18. Srinivas, N. and Deb, K.
    [1994] Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.
    [Google Scholar]
  19. Van Essen, G.M., Zandvliet, M.J., Van den Hof, P.M.J., Bosgra, O.H. and Jansen, J.D.
    [2009] Robust waterflooding optimization of multiple geological scenarios. SPE Journal, 14(1), 202–210.
    [Google Scholar]
  20. Wang, C., Li, G. and Reynolds, A.C.
    [2007] Optimal well placement for production optimization, SPE-111154. Proceedings of the SPE Eastern Regional Meeting, 17–19 October2007, Lexington, Kentucky, doi: 10.2118/111154‑MS.
    https://doi.org/10.2118/111154-MS [Google Scholar]
  21. Yeten, B., Durlofsky, L. and Aziz, K.
    [2003] Optimization of nonconventional well type, location and trajectory, SPE-86880. SPE Journal, 8(3), 200–210.
    [Google Scholar]
  22. Yeten, B.
    [2003] Optimum Deployment of Nonconventional Wells. Ph.D. thesis, Stanford University.
    [Google Scholar]
  23. Zitzler, E., Deb, K. and Thiele, L.
    [2000] Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2), 173–195.
    [Google Scholar]
  24. Zitzler, E., Laumanns, M. and Bleuler, S.
    [2004] A Tutorial on Evolutionary Multiobjective Optimization. Metaheuristics for Multiobjective Optimisation, Springer, vol. 535 of Lecture Notes in Economics and Mathematical Systems.
    [Google Scholar]
  25. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M. and Grunert da Fonseca, V.
    [2003] Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation, 7(2), 117–132.
    [Google Scholar]
  26. Zitzler, E. and Thiele, L.
    [1998] An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Tech. Rep. 43, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Gloriastrasse 35, CH-8092 Zurich, Switzerland.
    [Google Scholar]

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