Handling Geological Uncertainty in Oil Field Development with Multi-objective Optimization Technique

References

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]