Skip to main content
Log in

Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

The production life of oil reservoirs starts under significant uncertainty regarding the actual economical return of the recovery process due to the lack of oil field data. Consequently, investors and operators make management decisions based on a limited and uncertain description of the reservoir. In this work, we propose a new formulation for robust optimization of reservoir well controls. It is inspired by the multiple shooting (MS) method which permits a broad range of parallelization opportunities and output constraint handling. This formulation exploits coherent risk measures, a concept traditionally used in finance, to bound the risk on constraint violation. We propose a reduced sequential quadratic programming (rSQP) algorithm to solve the underlying optimization problem. This algorithm exploits the structure of the coherent risk measures, thus a large set of constraints are solved within sub-problems. Moreover, a variable elimination procedure allows solving the optimization problem in a reduced space and an iterative active-set method helps to handle a large set of inequality constraints. Finally, we demonstrate the application of constraints to bound the risk of water production peaks rather than worst-case satisfaction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Aitokhuehi, I., Durlofsky, L.J.: Optimizing the performance of smart wells in complex reservoirs using continuously updated geological models. J. Pet. Sci. Eng. 48(3-4), 254–264 (2005)

    Article  Google Scholar 

  2. Alhuthali, A.H., Datta-Gupta, A., Yuen, B., Fontanilla, J.P.: Optimizing smart well controls under geologic uncertainty. J. Pet. Sci. Eng. 73(1-2), 107–121 (2010)

    Article  Google Scholar 

  3. Artzner, P., Delbaen, F., Eber, J.-M., Heath, D.: Coherent measures of risk. Math. Financ. 9(3), 203–228 (1999)

    Article  Google Scholar 

  4. Bailey, W.J., Couet, B.: Field optimization tool for maximizing asset value. SPE Reserv. Eval. Eng. 8(1), 7–21 (2005)

    Article  Google Scholar 

  5. Basova, H.G., Rockafellar, R., Royset, J.O.: A Computational Study of the Buffered Failure Probability in Reliability-Based Design Optimization. In: Faber, M., Koehler, J., Nishijima, K. (eds.) Proceedings of the 11Th International Conference on Application of Statistics and Probability in Civil Engineering. Taylor & Francis, Zurich, Switzerland (2011)

    Google Scholar 

  6. Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton Series in Applied Mathematics. Princeton University Press, Princeton (2009)

    Google Scholar 

  7. Bertsimas, D., Brown, D.B., Caramanis, C.: Theory and applications of robust optimization. SIAM Rev. 53(3), 464–501 (2011)

    Article  Google Scholar 

  8. Biegler, L., Schmid, C., Ternet, D.: A Multiplier-Free, Reduced Hessian Method for Process Optimization. In: Biegler, L.T., Coleman, T., Conn, A.R., Santosa, F. (eds.) Large-Scale Optimization with Applications. Vol. 93 of the IMA Volumes in Mathematics and Its Applications. Springer New York, pp 101–127 (1997)

    Chapter  Google Scholar 

  9. Biegler, L.T., Nocedal, J., Schmid, C.: A reduced hessian method for large-scale constrained optimization. SIAM J. Optim. 5(2), 314–347 (1995)

    Article  Google Scholar 

  10. Biegler, L.T., Nocedal, J., Schmid, C., Ternet, D.: Numerical experience with a reduced Hessian method for large scale constrained optimization. Comput. Optim. Appl. 15(1), 45–67 (2000)

    Article  Google Scholar 

  11. Binder, T., Blank, L., Bock, H.G., Bulirsch, R., Dahmen, W., Diehl, M., Kronseder, T., Marquardt, W., Schlöder, J.P., Stryk, O.: Introduction to model based optimization of chemical processes on moving horizons. In: Grötschel, M., Krumke, S., Rambau, J (eds.) Online Optimization of Large Scale Systems, pp 295–339. Springer, Berlin (2001)

    Chapter  Google Scholar 

  12. Brouwer, D., Jansen, J.: Dynamic optimization of waterflooding with smart wells using optimal control theory. SPE J. 9(04), 391–402 (2004)

    Article  Google Scholar 

  13. Capolei, A., Foss, B., Jørgensen, J.B.: Profit and risk measures in oil production optimization 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production. Florianopolis, Brazil (2015a)

  14. Capolei, A., Suwartadi, E., Foss, B., Jørgensen, J.B.: A mean-variance objective for robust production optimization in uncertain geological scenarios. J. Pet. Sci. Eng. 125, 23–37 (2015b)

  15. Chamberlain, R., Powell, M., Lemarechal, C., Pedersen, H.: The Watchdog Technique for Forcing Convergence in Algorithms for Constrained Optimization. In: Buckley, A., Goffin, J. (eds.) Algorithms for Constrained Minimization of Smooth Nonlinear Functions. Vol. 16 of Mathematical Programming Studies. Springer Berlin Heidelberg, pp 1–17 (1982)

    Chapter  Google Scholar 

  16. Charnes, A., Cooper, W.W.: Chance-constrained programming. Manag. Sci. 6(1), 73–79 (1959)

    Article  Google Scholar 

  17. Charnes, A., Cooper, W.W., Symonds, G.H.: Cost horizons and certainty equivalents: an approach to stochastic programming of heating oil. Manag. Sci. 4(3), 235–263 (1958)

    Article  Google Scholar 

  18. Chen, C., Li, G., Reynolds, A.: Robust constrained optimization of short- and long-term net present value for closed-loop reservoir management. SPE J. 17(3), 849–864 (2012)

    Article  Google Scholar 

  19. Chen, Y., Oliver, D.S., Zhang, D.: Efficient ensemble-based closed-loop production optimization. SPE J. 14(4), 634–645 (2009)

    Article  Google Scholar 

  20. Codas, A.: Reservoir multiple shooting optimization code and cases. https://github.com/iocenter/remso (2014)

  21. Codas, A., Foss, B., Camponogara, E.: Output-constraint handling and parallelization for oil-reservoir control optimization by means of multiple shooting. SPE J. 20(4), 856–871 (2015)

    Article  Google Scholar 

  22. Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99, 10143 (1994)

    Article  Google Scholar 

  23. Fonseca, R., Kahrobaei, S., van Gastel, L., Leeuwenburgh, O., Jansen, J.: Quantification of the Impact of Ensemble Size on the Quality of an Ensemble Gradient Using Principles of Hypothesis Testing In: SPE Reservoir Simulation Symposium (2015)

  24. Fonseca, R., Stordal, A., Leeuwenburgh, O., Van Den Hof, P., Jansen, J.: Robust ensemble-based multi-objective optimization 14H European Conference on Mathematics of Oil Recovery. September 2014. Catania, Sicily, Italy, pp 1–14 (2014)

  25. Hanssen, K.G., Foss, B., Teixeira, A.: Production optimization under uncertainty with constraint handling 2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production (2015)

    Google Scholar 

  26. Heirung, T.A.N., Wartmann, M.R., Jansen, J.D., Ydstie, B.E., Foss, B.A.: Optimization of the Water-Flooding Process in a Small 2D Horizontal Oil Reservoir by Direct Transcription Proceedings of the 18Th IFAC World Congress, vol. 9, pp 10863–10868. Milano, Italy (2011)

  27. Hou, J., Zhou, K., Zhang, X.-S., Kang, X.-D., Xie, H.: A review of closed-loop reservoir management. Pet. Sci. 12(1), 114–128 (2015)

    Article  Google Scholar 

  28. Jansen, J.: Adjoint-based optimization of multi-phase flow through porous media: a review. Comput. Fluids 46(1), 40–51 (2011)

    Article  Google Scholar 

  29. Jansen, J., Brouwer, D., Nævdal, G., Van Kruijsdijk, C.: Closed-loop reservoir management. First Break 23(1), 43–48 (2005)

  30. Jansen, J.D.: A systems description of flow through porous media. Springer (2013)

  31. Jansen, J.-D., Brouwer, R., Douma, S.: Closed Loop Reservoir Management Proceedings of SPE Reservoir Simulation Symposium. Society of Petroleum Engineers the Woodlands, Texas (2009)

    Google Scholar 

  32. Jansen, J.D., Fonseca, R.M., Kahrobaei, S., Siraj, M., Van Essen, G., Van Den Hof, P.: The Egg Model. Tech. rep., Delft University of Technology, The Netherlands, Research note and data set. http://repository.tudelft.nl/view/ir/uuid:1b85ee17-3e58-4fa4-be79-8328945a4491 http://repository.tudelft.nl/view/ir/uuid:1b85ee17-3e58-4fa4-be79-8328945a4491 (report); http://data.3tu.nl/repository/uuid:916c86cd-3558-4672-829a-105c62985ab2 http://data.3tu.nl/repository/uuid:916c86cd-3558-4672-829a-105c62985ab2(data) (2013)

  33. Kourounis, D., Durlofsky, L.J., Jansen, J.D., Aziz, K.: Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow. Comput. Geosci. 18(2), 117–137 (2014)

    Article  Google Scholar 

  34. Kraaijevanger, J., Egberts, P., Valstar, J., Buurman, H.W.: Optimal Waterflood Design Using the Adjoint Method SPE Reservoir Simulation Symposium. Society of Petroleum Engineers (2007)

    Google Scholar 

  35. Krogstad, S., Raynaud, X., Nilsen, H.M.A., Lie, K.-A., Møyner, O., Skaflestad, B.: MRST-AD an open-source framework for rapid prototyping and evaluation of reservoir simulation problems SPE Reservoir Simulation Symposium (2015)

    Google Scholar 

  36. Leeuwenburgh, O., Egberts, P.J.P., Alin, G.C.: An ensemble-based method for constrained reservoir life-cycle optimization EUROPEC 2015. Society of Petroleum Engineers (2015)

  37. 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 (2011)

    Article  Google Scholar 

  38. Liu, X., Reynolds, A.C.: Multiobjective optimization for maximizing expectation and minimizing uncertainty or risk with application to optimal well control SPE Reservoir Simulation Symposium 2014. Society of Petroleum Engineers (2015a)

  39. Liu, X., Reynolds, A.C.: Pareto optimal solutions for minimizing risk and maximizing expected value of life-cycle npv of production under nonlinear constraints SPE Reservoir Simulation Symposium 2014. Society of Petroleum Engineers (2015b)

  40. Naevdal, G., Johnsen, L.M., Aanonsen, S.I., Vefring, E.H.: Reservoir monitoring and continuous model updating using ensemble kalman filter. SPE J. 10(01), 66–74 (2005)

    Article  Google Scholar 

  41. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer Series in Operations Research and Financial Engineering. Springer, New York (2006)

    Google Scholar 

  42. Oliver, D., Reynolds, A., Liu, N.: Inverse Theory for Petroleum reservoir Characterization and History Matching. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  43. Prėkopa, A.: Stochastic Programming, vol. 10. Springer, Netherlands (1995)

  44. Ramirez, W.: Application of optimal control theory to enhanced oil recovery. Elsevier Science (1987)

  45. Rockafellar, R., Royset, J.: On buffered failure probability in design and optimization of structures. Reliab. Eng. Syst. Saf. 95(5), 499–510 (2010)

    Article  Google Scholar 

  46. Rockafellar, R.T.: Coherent approaches to risk in optimization under uncertainty. INFORMS Tutorials in Operations Research, 38–61 (2007)

  47. Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000)

    Article  Google Scholar 

  48. Rockafellar, R.T., Uryasev, S., Zabarankin, M.: Deviation Measures in Risk Analysis and Optimization. Tech. Rep. Department of Industrial and Systems Engineering. University of Florida, Gainesville (2002)

    Google Scholar 

  49. Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on stochastic programming: modeling and theory (2009)

  50. Shapiro, A., Philpott, A.: A tutorial on stochastic programming. www.isye.gatech.edu/people/faculty/Alex_Shapiro/TutorialSP.pdf (2007)

  51. Siraj, M., Van den Hof, P., Jansen, J.D.: Model and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Water-Flooding Optimization SPE Reservoir Simulation Symposium (2015)

  52. Ternet, D.J., Biegler, L.T.: Recent improvements to a multiplier-free reduced Hessian successive quadratic programming algorithm. Comput. Chem. Eng. 22(7-8), 963–978 (1998)

    Article  Google Scholar 

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

  54. Yasari, E., Pishvaie, M.R.: Pareto-based robust optimization of water-flooding using multiple realizations. J. Pet. Sci. Eng. 132, 18–27 (2015)

    Article  Google Scholar 

  55. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrés Codas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Codas, A., Hanssen, K.G., Foss, B. et al. Multiple shooting applied to robust reservoir control optimization including output constraints on coherent risk measures. Comput Geosci 21, 479–497 (2017). https://doi.org/10.1007/s10596-017-9625-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-017-9625-4

Keywords

Navigation