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Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

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Abstract

Optimal well placement is critical to oil and gas field development. Typical workflows involve procedures to place a new well or a group of new wells in a reservoir in order to maximize some pre-defined reservoir performance metric. However, there are two main drawbacks with these traditional optimization approaches: First, the impact of geological uncertainty is often neglected or there may be no framework to include geological uncertainty. Second, traditional optimization techniques normally cannot meet the requirement of optimizing two or more conflicting objectives simultaneously—this may be useful when maximizing oil recovery while also minimizing water production. Consequently, in recent years, multiple objective optimization to obtain robust solutions that minimize the decision risk under geological uncertainty becomes a topic of renewed interest. Therefore, in this work, we develop a new work flow for well placement optimization while considering geological uncertainty in reservoir models. In general, when considering geological uncertainty, the primary goal is to maximize the mean net present value (NPV) over all realizations. However, restricting the search to simply maximizing the mean NPV may be inappropriate or inadequate for decision-making. A more reasonable choice is to maximize the mean NPV while minimizing the spread of the optimal NPV’s obtained for each realization. Therefore, in this work, we apply multi-objective optimization techniques to maximize the mean and minimize the variance of NPV values over all geological realizations to provide robust well placement solutions for decision-makers to select according to their risk attitude towards field development plans.

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References

  1. Aanonsen, S.I., Eide, A.L., Holden, L.: Optimizing reservoir performance under uncertainty with application to well location, SPE Annual Technical Conference and Exhibition, Dallas, USA, Oct 22–25, 1995 (1995)

  2. Artus, V., Durlofsky, L.J., Onwunalu, J., Aziz, K.: Optimization of nonconventional wells under uncertainty using statistical proxies 10(4):389–404 (2006)

  3. Bangerth, W., Klie, H., Wheeler, M.F., Stoffa, P., Sen, M.: On optimization algorithms for the reservoir oil well placement problem. Comput. Geosci. 10(3), 303–319 (2006)

    Article  Google Scholar 

  4. Beckner, B., Song, X.: Field development planning using simulated annealing—optimal economic well scheduling and placement, SPE-30650. In: 1995 SPE annual technical conference exhibition, Dallas, USA, pp 209–221 (1995)

  5. Bittencourt, A.C., Horne, R.N.: Reservoir development and design optimization SPE-38895. In: SPE annual technical conference exhibition, San Antonio, Texas, USA (1997)

  6. Bléhaut, J.F.: The assessment of geological uncertainties in development project planning (1991)

  7. Bouzarkouna, Z.: Well placement optimization, Ph.D. thesis. University of Paris-Sud, Paris (2012)

  8. Bouzarkouna, Z., Ding, D.Y., Auger, A.: Well placement optimization under uncertainty with cma-es using the neighborhood. In: 13th european conference on the mathematics of oil recovery (ECMOR XIII), EAGE (2012)

  9. Bouzarkouna, Z., Ding, D.Y., Auger, A.: Partially separated metamodels with evolution strategies for well-placement optimization, SPE Journal (SPE-143292-PA) (2013)

  10. Cottini-Loureiro, A., Araujo, A.: Optimized well location by combination of multiple realization approach and quality map methods. In: 2005 SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA (2005)

  11. Cruz, P.S., Horne, R.N., Deutsch, C.V.: The quality map: a tool for reservoir uncertainty quantification and decision making. In: SPE annual technical conference and exhibition, Houston, TX, USA, Oct 3–6, 1999 (1999)

  12. Cruz, P.S., Horne, R.N., Deutsch C.V.: The quality map: a tool for reservoir uncertainty quantification and decision making. SPE Reservoir Evaluation & Engineering (2004)

  13. Das, I., Dennis, J.: A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems. Struct. Optim. 14, 63–69 (1997)

    Article  Google Scholar 

  14. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)

    Google Scholar 

  15. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  16. Devegowda, D., Gao H.: Integrated uncertainty assessment for unconventional gas reservoir project development (SPE-111203). In: 2007 SPE Eastern Regional Meeting, Lexington, Kentucky, USA (2007)

  17. Emerick, A.A., PS, A., Silva, E., Messer, B., Almeida, L.F.: (2009) Well placement optimization using a genetic algorithm with nonlinear constraints. In: 2009 SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA

  18. Emmerich, M., Beume, N., Naujoks, B.: An emo algorithm using the hypervolume measure as selection criterion. In: 2005 International Conference, pp 62–76. Springer (2005)

  19. Ferraro, P., Verga, F.: Use of evolutionary algorithms in single and multi-objective optimization techniques for assisted history matching. In: Proceeding of the offshore mediterranean conference and exhibition in Ravenna, Italy, March 25–27, 2009 (2009)

  20. Ferreira, J., Fonseca, C., Gaspar-Cunha, A.: Methodology to select solutions from the Pareto-optimal set: a comparative study. In: Thierens, D. (ed.) 2007 genetic and evolutionary computation Conference (GECCO’2007), vol. 1, pp 789–796. ACM Press, London (2007)

    Google Scholar 

  21. Fonseca, R., Leeuwenburgh, O., Jansen, J.: Ensemble based multi-objective production optimization of smart wells. In: Proceedings of the 13th european conference on the mathematics of oil recovery (ECMOR XIII), Biarritz, France, 10–13 September, 2012 (2012)

  22. Forouzanfar, F., Li, G., Reynolds, A. C.: A two-stage well placement optimization method based on adjoint gradient. In: SPE annual technical conference and exhibition, Florence, Italy (2010)

  23. Guyaguler, B., Horne, R.: Optimization of well placement. J. Energy Res. Technol. 122, 64–70 (2000)

    Article  Google Scholar 

  24. Guyaguler, B., Horne, R.N.: Uncertainty assessment of well-placement optimization. SPE Reserv. Eval. Eng. 7(1), 24–32 (2004)

    Article  Google Scholar 

  25. Hajizadeh, Y., Christie, M., Demyanov, V.: Towards multiobjective history matching: faster convergence and uncertainty quantification, SPE-141111. SPE Reservoir Simulation, The Woodlands, Texas, USA, 21-23 February 2011 (2011). https://www.onepetro.org/conference-paper/-MS

  26. Han-Young Park, A.D.G., King, M.J.: Handling conflicting multiple objectives using pareto-based evolutionary algorithm during history matching of reservoir performance (SPE 163623). In: SPE Reservoir Simulation Symposium. The Woodlands, Texas (2013)

  27. Handels, M., Zandvliet, M.J., Brouwer, D.R., Jansen, J.D.: Adjoint-based well-placement optimization under production constraints (SPE 105797). In: Proceedings of the SPE reservoir simulation symposium, Houston, Texas, USA, 26–28 February 2007 (2007)

  28. Isebor, O.J., Durlofsky, L.J.: Biobjective optimization for general oil field development, Journal of Petroleum Science and Engineering (2014)

  29. Ishibuchi H., Tsukamoto N., Nojima Y.: Evolutionary many-objective optimization: a short review. In: IEEE Congress on Evolutionary Computation, pp 2419–2426. IEEE (2008)

  30. Leeuwenburgh, O., Egberts, P.J.P., Abbink, O.A.: Ensemble methods for reservoir life-cycle optimization and well placement (SPE 136916). In: Proceedings of SPE/DGS annual technical and symposium and exhibition, 04–07 April 2010, Al-Khobar, Saudi Arabia (2010)

  31. Liu, N., Jalali, Y.: Closing the loop between reservoir modeling and well placement and positioning (2006)

  32. Liu, N., Betancourt, S., Oliver, D.S.: Assessment of uncertainty assessment methods. In: Proceedings of the 2001 SPE annual technical conference and exhibition, pp 1–15 (2001)

  33. Marler, R., Arora, J.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. April 2004 26(6), 369–395 (2004)

    Article  Google Scholar 

  34. Maschio, C., Vidal, A.C., Schiozer, D.J.: A framework to integrate history matching and geostatistical modeling using genetic algorithm and direct search methods. J. Petrol. Sci. Eng. 63(1–4), 34–42 (2008). doi:10.1016/j.petrol.2008.08.001

    Article  Google Scholar 

  35. Mohamed, L., Christie, M., Demyanov, V.: History matching and uncertainty quantification: multiobjective particle swarm optimisation approach, (SPE 143067). In: Proceedings of the SPE EUROPEC/EAGE annual conference and exhibition, Vienna, Austria, 23–26 May 2011 (2011)

  36. Nakajima, L., Schiozer, D.: Horizontal well placement optimization using quality map definition. In: SPE Canadian international petroleum conference, Alberta Canada June 10–12, 2003 (2003)

  37. Onwunalu, J., Durlofsky, L.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci. 2010(14), 183–198 (2009). doi:10.1007/s10596-009-9142-1

    Google Scholar 

  38. Ozdogan, U., Horne, R.N.: Optimization of well placement under time-dependent uncertainty (2006)

  39. Raghuwanshi, M.M., Kakde, O.G.: Survey on multiobjective evolutionary and real coded genetic algorithms. In: Proceedings of the 8th Asia Pacic symposium on intelligent and evolutionasy systems, pp 150–161 (2004)

  40. Sarma, P., Durlofsky, L.J., Aziz, K.: Kernel principal component analysis for efficient, differentiable parameterization of multipoint geostatistics. Math. Geosci. 40(1), 3–32 (2008). doi:10.1007/s11004-007-9131-7

    Article  Google Scholar 

  41. Schulze-Riegert, R., Krosche, M., Fahimuddin, A., Ghedan, S.: Multi-objective optimization with application to model validation and uncertainty quantification. In: SPE Middle East oil and gas show and conference, 11–14 March 2007, Kingdom of Bahrain (2007)

  42. Schulze-Riegert, R., Dong, M., Heskestad, K.L., Krosche, M., Mustafa, H., Stekolschikov, K., Bagheri, M.: Well path design optimization under geological uncertainty: application to complex north sea field. In: 2010 SPE Russian oil & gas technical conference and exhibition, Moscow, Russia (2010)

  43. Seshadri, A.: A fast elitist multiobjective genetic algorithm: NSGA-II, class project. Oklahoma State University (2006)

  44. Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  45. Van Essen, G.M., Zandvliet, M.J., Van den Hof, P.M.J., Bosgra, O.H., Jansen, J.D.: Robust waterflooding optimization of multiple geological scenarios. SPE J. 14(1), 202–210 (2009)

    Article  Google Scholar 

  46. Wang, C., Li, G., Reynolds, A.C.: Optimal well placement for production optimization, SPE-111154. In: Proceedings of the SPE Eastern Regional Meeting, 17–19 October 2007, Lexington, Kentucky (2007), doi:10.2118/111154-MS

  47. Wang, H., Ciaurri, D.E., Durlofsky, L.J., Cominelli, A.: Optimal well placement under uncertainty using a retrospective optimization framework (SPE 141950). In: SPE reservoir simulation symposium, pp 1–19. The Woodlands, Texas (2011)

  48. Yeten, B.: Optimum deployment of nonconventional wells, PhD thesis. Stanford University (2003)

  49. Yeten, B., Durlofsky, L., Aziz, K.: Optimization of nonconventional well type, location and trajectory, SPE-86880. SPE J. 8(3), 200–210 (2003)

    Article  Google Scholar 

  50. Yoon, K., Hwang, C.L.: Multiple attribute decision making, an introduction. SAGE Publications, Inc, London (1995)

    Google Scholar 

  51. Zandvliet, M., Handels, M., van Essen, G., Brouwer, D., Jansen, J.: Adjoint-based well-placement optimization under production constraints. SPE J. 13, 392–399 (2008)

    Article  Google Scholar 

  52. Zitzler, E., Thiele, L.: 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 (1998)

  53. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  54. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

  55. Zitzler, E., Laumanns, M., Bleuler, S., et al. Gandibleux, X. (ed.): A tutorial on evolutionary multiobjective optimization, vol. 535. Springer (2004). Lecture notes in economics and mathematical systems

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Chang, Y., Bouzarkouna, Z. & Devegowda, D. Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty. Comput Geosci 19, 933–950 (2015). https://doi.org/10.1007/s10596-015-9507-6

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