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Well placement optimization subject to realistic field development constraints

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Abstract

This work considers the well placement problem in reservoir management and field development optimization. In particular, it emphasizes embedding realistic and practical constraints into a mathematical optimization formulation. Such constraints are a prerequisite for the wider use of mathematical optimization techniques in well placement problems, since constraints are a way to incorporate reservoir engineering knowledge into the problem formulation. There are important design limitations that are used by the field development team when treating the well placement problem, and these limitations need to be articulated and eventually formalized within the problem before conducting the search for optimal well placements. In addition, these design limitations may be explicit or implicit. In this work, various design limitations pertaining to well locations have been developed in close collaboration with a field operator on the Norwegian Continental Shelf. Moreover, this work focuses on developing constraint-handling capability to enforce these various considerations during optimization. In particular, the Particle Swarm Optimization (PSO) algorithm is applied to optimize for the well locations, and various practical well placement constraints are incorporated into the PSO algorithm using two different constraint-handling techniques: a decoder procedure and the penalty method. The decoder procedure maps the feasible search space onto a cube and has the advantage of not requiring parameter tuning. The penalty method converts the constrained optimization problem into an unconstrained one by introducing an additional term, which is called a penalty function, to the objective function. In contrast to the penalty method, only feasible solutions are evaluated in the decoder method. Through numerical simulations, a comparison between the penalty method and the decoder technique is performed for two cases. We show that the decoder technique can easily be implemented for the well placement problem, and furthermore, that it performs better than the penalty method in most of the cases.

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References

  1. Jansen, J.D., Brouwer, D.R., Naevdal, G., van Kruijsdijk, C.P.J.W.: Closed-loop reservoir management. First Break 23(1), 43–48 (2005)

    Google Scholar 

  2. Foss, B., Jensen, J.P.: Performance analysis for closed loop reservoir management. SPE J. 16(1), 183–190 (2011)

    Article  Google Scholar 

  3. Shirangi, M.G., Durlofsky, L.J.: Closed-loop field development under uncertainty by use of optimization with sample validation. SPE J. 20(5), 908–922 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Forouzanfar, F., Reynolds, A.C.: Joint optimization of number of wells, well locations and controls using a gradient-based algorithm. Chem. Eng. Res. Des. 92(7), 315–1328 (2014)

    Article  Google Scholar 

  6. Petvipusit, K.R., Chang, Y.: Dynamic well conversion and rate optimisation using ensemble-based method. In: SPE Bergen One Day Seminar, 22 April, Bergen, Norway. Society of Petroleum Engineers (2015)

  7. Bellout, M.C., Ciaurri, D.E., Durlofsky, L.J., Foss, B., Kleppe, J.: Joint optimization of oil well placement and controls. Comput. Geosci. 16(4), 1061–1079 (2012)

    Article  Google Scholar 

  8. Navabi, S., Khaninezhad, R., Jafarpour, B.: A generalized formulation for oilfield development optimization. In: 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production (2015)

  9. Li, L., Jafarpour, B.: A variable-control well placement optimization for improved reservoir development. Comput. Geosci. 16(4), 871–889 (2012)

    Article  Google Scholar 

  10. Jesmani, M., Foss, B.: Use of time-varying oil price in short-term production optimization for a reservoir. In: 10th IFAC International Symposium on Dynamics and Control of Process Systems, pp. 619–624. India (2013)

  11. Isebor, O.J., Durlofsky, L.J., Ciaurri, D.E.: A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls. Comput. Geosci. 18(3–4), 463–482 (2014)

    Article  Google Scholar 

  12. Kharghoria, A., Cakici, M., Narayanasamy, R., Kalita, R., Sinha, S., Jalali, Y.: Productivity-based method for selection of reservoir drilling target and steering strategy. In: SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates. Society of Petroleum Engineers (2003)

  13. Seydoux, J., Legendre, E., Mirto, E., Dupuis, C., Denichou, J.-M., Bennett, N., Kutiev, G., Kuchenbecker, M., Morriss, C., Yang, L.: Full 3D deep directional resistivity measurements optimize well placement and provide reservoir-scale imaging while drilling. Society of petrophysicists and well-log analysts. In: SPWLA 55th Annual Logging Symposium, Abu Dhabi, United Arab Emirates. Society of Petrophysicists and Well-Log Analysts (2014)

  14. Bourgeois, D., Tribe, I., Christensen, R., Durbin, P., Kumar, S., Skinner, G., Wharton, D.: Improving well placement with modeling while drilling. Oilfield Review 17(3), 54–63 (2005)

    Google Scholar 

  15. Koziel, S., Michalewicz, Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol. Comput. 7(1), 19–44 (1999)

    Article  Google Scholar 

  16. Onwunalu, J.E., Durlofsky, L.J.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput. Geosci. 14(1), 183–198 (2010)

    Article  Google Scholar 

  17. Bangerth, W., Klie, H., Wheeler, M.F., Stoffa, P.L., Sen, M.K.: On optimization algorithms for the reservoir oil well placement problem. Comput. Geosci. 10(3), 303–319 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Bouzarkouna, Z., Ding, D.Y., Auger, A.: Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models. Comput. Geosci. 16(1), 75–92 (2012)

    Article  Google Scholar 

  21. Kennedy, J., Eberhart, R.C.: Partical swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948 (1995)

  22. Wang, H., Echeverría-Ciaurri, D., Durlofsky, L.J., Cominelli, A.: Optimal well placement under uncertainty using a retrospective optimization framework. SPE J. 17(1), 112–121 (2012)

    Article  Google Scholar 

  23. Li, L., Jafarpour, B., Mohammad-Khaninezhad, M.R: A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty. Comput. Geosci. 17(1), 167–188 (2013)

    Article  Google Scholar 

  24. Sarma, P., Chen, W.H.: Efficient well placement optimization with gradient-based algorithms and adjoint models. In: Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands. Society of Petroleum Engineers (2008)

  25. Jesmani, M., Bellout, M.C., Hanea, R., Foss, B.: Particle swarm optimization algorithm for optimum well placement subject to realistic field development constraints. In: SPE Reservoir Characterisation and Simulation Conference and Exhibition. Society of Petroleum Engineers, Abu Dhabi, UAE (2015)

  26. Awotunde, A.A., Naranjo, C.: Well placement optimization constrained to minimum well spacing. In: SPE Latin America and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela. Society of Petroleum Engineers (2014)

  27. Kennedy, J., Mendes, R.: Population Structure and Particle Swarm Performance. In: Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002 Congress on, vol. 2, pp 1671–1676 (2002)

  28. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: The 1998 IEEE International Conference on Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, pp 69–73 (1998)

  29. Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, vol. 3 (1999)

  30. El-Gallad, A.I., El-Hawary, M., Sallam, A.A.: Swarming of intelligent particles for solving the nonlinear constrained optimization problem. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communication 9(3), 155–163 (2001)

    Google Scholar 

  31. Hu, X., Eberhart, R.C.: Solving constrained nonlinear optimization problems with particle swarm optimization. In: Proceedings of the Sixth World Multiconference on Systemics, Cybernetics and Informatics 2002 (SCI 2002), Orlando, USA (2002)

  32. Litvak, M.L., Angert, P.F.: Field development optimization applied to giant oil fields. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers (2009)

  33. Emerick, A.A., Silva, E., Messer, B., Almeida, L.F., Szwarcman, D., Pacheco, M.A.C., Vellasco, M.M.B.R.: Well placement optimization using a genetic algorithm with nonlinear constraints. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers (2009)

  34. Bellout, M.C.: Joint Optimization of Oil Well Placement and Controls. PhD thesis, Norwegian University of Science and Technology (NTNU) (2014)

  35. Bo, Y., Chen, Y., Zhao, Z., Han, Q.: A master-slave particle swarm optimization algorithm for solving constrained optimization problems. In: Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on, vol. 1, pp 3208–3212 (2006)

  36. Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)

    Article  Google Scholar 

  37. Echeverría Ciaurri, D., Isebor, O.J., Durlofsky, L.J.: Application of derivative-free methodologies to generally constrained oil production optimization problems. Procedia Computer Science 1(1), 1301–310 (2010)

    Article  Google Scholar 

  38. Humphries, T.D., Haynes, R.D.: Joint optimization of well placement and control for nonconventional well types. J. Pet. Sci. Eng., 126 (2015)

  39. Alghareeb, Z.M., Walton, S.P., Williams, J.R.: Well placement optimization under constraints using modified cuckoo search. In: SPE Saudi Arabia Section Technical Symposium and Exhibition. Society of Petroleum Engineers (2014)

  40. Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. In: Proceedings of Euro-International Symposium on Computational Intelligence 2002 (2002)

  41. Ben Hamida, S., Schoenauer, M.: An Adaptive Algorithm for Constrained Optimization Problems. Springer (2000)

  42. Homaifar, A., Qi, C.X., Lai, S.H.: Constrained optimization via genetic algorithms. Simulation 62(4), 242–253 (1994)

    Article  Google Scholar 

  43. Carosio, G.L.C., Humphries, T.D., Haynes, R.D., Farquharson, C.G.: A closer look at differential evolution for the optimal well placement problem. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference (2015)

  44. Joines, J.A., Houck, C.R.: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with ga’s. In: Proceedings of the First IEEE Conference on IEEE World Congress on Computational Intelligence. IEEE (1994)

  45. Powell, D., Skolnick, M.M.: Using genetic algorithms in engineering design optimization with non-linear constraints. In: Proceedings of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann Publishers Inc (1993)

  46. Smith, A.E., Tate, D.M.: Genetic optimization using a penalty function. In: Proceedings of the 5Th International Conference on Genetic Algorithms. Morgan Kaufmann Publishers Inc (1993)

  47. Hadj-Alouane, A.B., Bean, J.C.: A genetic algorithm for the multiple-choice integer program. Oper. Res. 45(1) (1997)

  48. He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20(1), 89–99, 2 (2007)

    Article  Google Scholar 

  49. Riche, R.L., Knopf-Lenoir, C., Haftka, R.T.: A segregated genetic algorithm for constrained structural optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms (1995)

  50. Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer, New York (2006)

    Google Scholar 

  51. Richardson, J.T., Palmer, M.R., Liepins, G.E., Hilliard, M.: Some guidelines for genetic algorithms with penalty functions. In: Proceedings of the Third International Conference on Genetic Algorithms, pp 191–197. Morgan Kaufmann Publishers Inc, San Francisco, CA, USA (1989)

  52. Koziel, S., Michalewicz, Z.: A Decoder-Based Evolutionary Algorithm for Constrained Parameter Optimization Problems, vol. 1498 of Lecture Notes in Computer Science. Springer, Berlin, Heidelberg (1998)

    Google Scholar 

  53. Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization for minimax problems. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 2, pp 1576–1581, Honolulu, HI (2002)

  54. Wetter, M.: Genopt, Generic Optimization Program. Simulation Research Group, Building Technologies Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 3.1.0 edition (2011)

  55. Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers (2001)

  56. Christie, M.A., Blunt, M.J.: Tenth spe comparative solution project: a comparison of upscaling techniques. SPE Reserv. Evalu. Eng., 4 (2011)

  57. Møyner, O., Lie, K.-A., Krogstad, S.: Flow diagnostics for use in reservoir management. SPE J. 20(2), 306–323 (2015)

    Article  Google Scholar 

  58. Lie, K.-A., Krogstad, S., Ligaarden, I.S., Natvig, J.R., Nilsen, H.M., Skaflestad, B.: Open source Matlab implementation of consistent discretisations on complex grids. Comput. Geosci. 16(2), 297–322 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering Cybernetics, Center for Integrated Operations in the Petroleum Industry: Petroleum Cybernetics Group, NTNU, Trondheim, Norway

    Mansoureh Jesmani & Bjarne Foss

  2. Department of Petroleum Engineering and Applied Geophysics, Center for Integrated Operations in the Petroleum Industry: Petroleum Cybernetics Group, NTNU, Trondheim, Norway

    Mathias C. Bellout

  3. Statoil, Bergen, Norway

    Remus Hanea

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  1. Mansoureh Jesmani
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  2. Mathias C. Bellout
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  3. Remus Hanea
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  4. Bjarne Foss
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Correspondence to Mansoureh Jesmani.

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Jesmani, M., Bellout, M.C., Hanea, R. et al. Well placement optimization subject to realistic field development constraints. Comput Geosci 20, 1185–1209 (2016). https://doi.org/10.1007/s10596-016-9584-1

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