Skip to main content
Log in

Nonlinear output constraints handling for production optimization of oil reservoirs

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

Abstract

Adjoint-based gradient computations for oil reservoirs have been increasingly used in closed-loop reservoir management optimizations. Most constraints in the optimizations are for the control input, which may either be bound constraints or equality constraints. This paper addresses output constraints for both state and control variables. We propose to use a (interior) barrier function approach, where the output constraints are added as a barrier term to the objective function. As we assume there always exist feasible initial control inputs, the method maintains the feasibility of the constraints. Three case examples are presented. The results show that the proposed method is able to preserve the computational efficiency of the adjoint methods.

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. Chen, Y., Oliver, D.S., Zhang, D.: Efficient ensemble-based closed-loop production optimization. SPE J. 14(4), 634–645 (2008)

    Google Scholar 

  2. Bryson, A., Ho, Y.: Applied Optimal Control. Hemisphere, Washington, D.C. (1975)

    Google Scholar 

  3. Mehra, R., Davis, R.: A Generalized gradient method for optimal control problems with inequality constraints and singular arch. IEEE Trans. Automat. Contr. 17, 69–79 (1972)

    Article  MATH  Google Scholar 

  4. Hargraves, C., Paris, S.: Direct trajectory optimization using nonlinear programming and collocation. J. Guid. Control Dyn. 10(4), 338–342 (1987)

    Article  MATH  Google Scholar 

  5. Bloss, K.F., Biegler, L.T., Schiesser, W.E.: Dynamics process optimization through adjoint formulations and constraint aggregation. Ind. Eng. Chem. Res. 38(2), 421–432 (1999)

    Article  Google Scholar 

  6. Becerra, V.M.: Solving optimal control problems with state constraints using nonlinear programming and simulation tools. IEEE Trans. Ed. 43(3), 377–384 (2004)

    Google Scholar 

  7. Fiacco, A.V., McCormick, G.P.: Nonlinear Programming: Sequential Unconstrained Minimization Techniques. Wiley, New York (1968)

    MATH  Google Scholar 

  8. Forsgren, A., Gill, P.E., Wright, M.H.: Interior methods for nonlinear optimization. SIAM Rev. 44(4), 525–597 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kreisselmeier, G., Steinhauser, R.: Systematic Control Design by Optimizing a Vector Performance Index. IFAC Symposium on CADS, Zurich (1979)

  10. Griewank, A., Korzec, M.: Approximating Jacobians by the TR2 formula. Proc. Appl. Math. Mech. 5, 791–792 (2005)

    Article  Google Scholar 

  11. Hinze, M., Pinnau, R., Ulbrich, M., Ulbrich, S.: Optimization with PDE Constraints. Springer, New York (2009)

    Google Scholar 

  12. Virnovsky, G.A.: Waterflooding strategy design using optimal control theory. In: Proceeding of 6th. European IOR-symp. Stavanger, Norway (1991)

    Google Scholar 

  13. Zakirov, I.S., Aanonsen, S.I., Zakirov, E.S., Palatnik, B.M.: Optimizing reservoir performance by automatic allocation of well rates. In: Proceeding of 5th. ECMOR Leoben, Austria (1996)

  14. Jansen, J.D.: Adjoint-based optimization of multi-phase flow through porous media—a review. Comput. Fluids 46, 40–51 (2011). doi:10.1016/j.compfluid.2010.09.039

    Article  MATH  Google Scholar 

  15. Wu, Z.: Conditioning geostatistical models to two-phase flow production Data. Ph.D. thesis, University of Tulsa (1999)

  16. Rommelse, J.R.: Data assimilation in reservoir management. Ph.D. thesis, TU Delft (2009)

  17. Zandvliet, M.J., Handels, M., Van Essen, G.M., Brouwer, D.R., Jansen, J.D.: Adjoint-based well placement optimization under production constraints. SPE J. 13(4), 392–399 (2008)

    Google Scholar 

  18. Montleau, P. de., Cominelli, A. , Neylon, K., Rowan, D., Pallister, I., Tesaker, O., Nygard, I.: Production optimization under constraints using adjoint gradient. In: Proceedings of ECMOR X-10th European Conference on the Mathematics of Oil Recovery Number A041. EAGE, Amsterdam, The Netherlands (2006)

    Google Scholar 

  19. Sarma, P., Chen, W.H., Durlofsky, L.J., Aziz, K.: Production optimization with adjoint models under nonlinear control-state path inequality constraints. SPEREE, 11(2), 326–339 (2008). Paper SPE 99959

    Article  Google Scholar 

  20. Kraaijevanger, J.F.B.M., Egberts, P.J.P., Valstar, J.R., Buurman, H.W.: Optimal waterflood design using the adjoint method. Paper SPE 105764 presented at 2007 SPE RSS, Houston, TX, USA, 26–28 Feb (2007)

  21. Aziz, K., Settari, A.: Petroleum Reservoir Simulation. Applied Science, New York (1979)

    Google Scholar 

  22. Van Essen, G.M., Van den Hof, P.M.J., Jansen, J.D.: Hierarchical long-term and short-term production optimization. SPE J. 16(1), 191–199 (2011)

    Google Scholar 

  23. Chen, C., Li, G., Reynolds, A.: Robust constrained optimization of short and long-term NPV for closed-loop reservoir management. Paper SPE 141314 presented at 2011 SPE RSS, Houston, TX, USA, 21–23 Feb 2011

  24. Griewank, A., Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. SIAM, Philadelphia. ISBN 978-0-898716-59-7 (2008)

  25. Suwartadi, E., Krogstad, S., Foss, B.: A Lagrangian-barrier function for adjoint state constraints optimization of oil reservoirs water flooding. In: IEEE Conference on Proceeding of 2010 49th, pp. 3884–3889 (2010). doi:10.1109/CDC.2010.5717749

  26. Jittorntrum, K., Osborne, M.R.: A modified barrier function method with improved rate of convergence for degenerate problems. J. Austral. Math. Soc, Series B 21, 305–329

  27. Byrd, R.H., Nocedal, J., Waltz, R.A.: KNITRO: an integrated package for nonlinear optimization. In: Di Pillo, G., Roma, M. (eds.) Large-Scale Nonlinear Optimization. Springer, USA (2006)

    Google Scholar 

  28. Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust-Region Methods. SIAM, Philadelphia (2000)

    Book  MATH  Google Scholar 

  29. Steihaug, T.: The conjugate gradient method and trust regions in large scale optimization. SIAM J. Numer. Anal. 20, 626–637 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  30. Conn, A.R., Gould, N.I.M., Toint, P.L.: A globally convergent Lagrangian barrier algorithm for optimization with general inequality constraints and simple bounds. Math. Comput. 66(217), 216–288 (1997)

    MathSciNet  Google Scholar 

  31. Lie, K.-A., Krogstad, S., Ligaarden, I.S., Natvig, L.R., Nilsen, H.M., Skafltestad, B.: Open-source MATLAB implementation of consistent discretisations on complex grids. Comput. Geosci. (2011). doi:10.1007/s10596-011-9244-4

    Google Scholar 

  32. Christie, M.A., Blunt, M.J.: Tenth SPE comparative solution project: a comparative of upscaling technique. SPE Reserv. Evalu. Eng. 4, 308–317 (2001)

    Google Scholar 

  33. Rwechungura, R., Suwartadi, E., Dadashpour, M., Kleppe, J., Foss, B.: The Norne field case—a unique comparative case study. Paper SPE 127538 presented at 2010 SPE Intelligent Energy Conference and Exhibition, Utrecht, The Netherlands, 23–25 March 2010

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eka Suwartadi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Suwartadi, E., Krogstad, S. & Foss, B. Nonlinear output constraints handling for production optimization of oil reservoirs. Comput Geosci 16, 499–517 (2012). https://doi.org/10.1007/s10596-011-9253-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-011-9253-3

Keywords

Mathematics Subject Classifications (2010)

Navigation