1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVII
  5. Conference paper

Abstract

Summary

A Derivative-Free Trust-Region (DFTR) algorithm is proposed to solve for the well control optimization problem. Derivative-Free (DF) methods are often a practical alternative because gradients may not be available and/or are unreliable due to cost function discontinuities, e.g., caused by enforcement of simulation-based constraints. However, the effectiveness of DF methods for solving realistic cases is heavily dependent on an efficient sampling strategy since cost function calculations often involve time-consuming reservoir simulations. The DFTR algorithm samples the cost function space around an incumbent solution and builds a quadratic approximation model, valid within a bounded region (the trust-region). A minimization of the quadratic model guides the method in its search for descent. Crucially, because of the curvature information provided by the model-based routine, the trust-region approach is able to conduct a more efficient search compared to other sampling methods, e.g., direct-search approaches.

DFTR is implemented within FieldOpt, an open-source framework for field development optimization that provides flexibility with respect to problem parameterization and parallelization capabilities. DFTR is tested in the synthetic case Olympus against two other type of methods commonly applied to production optimization: a direct-search (Asynchronous Parallel Pattern Search) and a population-based (Particle Swarm Optimization). Current results show DFTR has promising convergence properties. In particular, the method is seen to reach fairly good solutions using only a few iterations. This feature can be particularly attractive for practitioners who seek ways to improve production strategies while using full-fledged models. Future work will focus on wider application of the algorithm in more complex field development problems such as joint problems and ICD optimization, and extensions to the algorithm to deal with multiple geological realizations and output constraints.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202035086
2020-09-14
2024-04-24

  • From This Site

    /content/papers/10.3997/2214-4609.202035086
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Baumann, E.J., Dale, S.I. and Bellout, M.C.
    [2020] FieldOpt: A powerful and effective programming framework tailored for field development optimization.Computers & Geosciences, 135, 104379.
     [Google Scholar]
  2. Brouwer, D.R. and lansen, J.D.
    [2004] Dynamic Optimization of Waterflooding With Smart Wells Using Optimal Control Theory.SPE Journal, 9(04), 391–402.
     [Google Scholar]
  3. Bukshtynov, V., Volkov, O., Durlofsky, L.J. and Aziz, K.
    [2015] Comprehensive framework for gradient-based optimization in closed-loop reservoir management.Computational Geosciences, 19(4), 877–897.
     [Google Scholar]
  4. Capolei, A., Suwartadi, E., Foss, B. and J0rgensen, J.B.
    [2013] Waterflooding optimization in uncertain geological scenarios.Computational Geosciences, 17(6), 991–1013.
     [Google Scholar]
  5. Chen, C, Li, G. and Reynolds, A.
    [2012] Robust Constrained Optimization of Short- and Long-Term Net Present Value for Closed-Loop Reservoir Management.SPE Journal.
     [Google Scholar]
  6. Codas, A., Foss, B. and Camponogara, E.
    [2015] Output-Constraint Handling and Parallelization for Oil-Reservoir Control Optimization by Means of Multiple Shooting.SPE Journal, 20(4), 856–871.
     [Google Scholar]
  7. Conn, A.R., Gould, N.I.M. and Toint, PL.
    [2000] Trust Region Methods. Society for Industrial and Applied Mathematics (SIAM) and Mathematical Programming Society (MPS).
     [Google Scholar]
  8. Conn, A.R., Scheinberg, K. and Toint, PL.
    [1997] Recent progress in unconstrained nonlinear optimization without derivatives.Mathematical Programming, 79(1–3), 397–414.
     [Google Scholar]
  9. Conn, A.R., Scheinberg, K. and Vicente, L.N.
    [2008] Geometry of interpolation sets in derivative free optimization.Mathematical Programming, 111(1–2), 141–172.
     [Google Scholar]
  10. [2009] Introduction to Derivative-Free Optimization. Society for Industrial and Applied Mathematics (SIAM) and Mathematical Programming Society (MPS).
     [Google Scholar]
  11. Datta-Gupta, A., Alhuthali, A.H.H., Yuen, B. and Fontanilla, J.
    [2010] Field Applications of Waterflood Optimization via Optimal Rate Control With Smart Wells.SPE Reservoir Evaluation & Engineering, 13(03), 406–422.
     [Google Scholar]
  12. Dehdari, V. and Oliver, D.S.
    [2012] Sequential Quadratic Programming for Solving Constrained Production Optimization-Case Study From Brugge Field.SPE Journal, 17(03), 874–884.
     [Google Scholar]
  13. Echeverria Ciaurri, D., Isebor, O. and Durlofsky, L.
    [2010] Application of derivative-free methodologies to generally constrained oil production optimization problems.Procedia Computer Science, 1(1), 1301–1310.
     [Google Scholar]
  14. van Essen, G., Van den Hof, P. and Jansen, J.D.
    [2011] Hierarchical Long-Term and Short-Term Production Optimization.SPE Journal, 16(01), 191–199.
     [Google Scholar]
  15. Fonseca, R., Rossa, E.D., Emerick, A., Hanea, R. and Jansen, J.
    [2018] Overview Of The Olympus Field Development Optimization Challenge. In: ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery. EAGE Publications BV, 1–10.
     [Google Scholar]
  16. GeoQuest, S.
    [2014] ECLIPSE reservoir simulator.Man. Tech. Descr. Houston, TX.
     [Google Scholar]
  17. Hasan, A., Gunnerud, V, Foss, B., Teixeira, A.F and Krogstad, S.
    [2013] Decision Analysis for Long-term and Short-term Production Optimization Applied to the Voador Field. In: Proc. of SPE Reservoir Characterization and Simulation Conference and Exhibition. Society of Petroleum Engineers, 16–18.
     [Google Scholar]
  18. Hough, P.D., Kolda, T.G. and Torczon, V.J.
    [2001] Asynchronous parallel pattern search for nonlinear optimization.SIAM Journal on Scientific Computing, 23(1), 134–156.
     [Google Scholar]
  19. Jansen, J.D.
    [2011] Adjoint-based optimization of multi-phase flow through porous media - A review.Computers & Fluids, 46(1), 40–51.
     [Google Scholar]
  20. Jansen, J.D., Brouwer, R. and Douma, S.G.
    [2009] Closed Loop Reservoir Management. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers.
     [Google Scholar]
  21. Kourounis, D., Durlofsky, L.J., Jansen, J.D. and Aziz, K.
    [2014] Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow.Computational Geosciences, 18(2), 117–137.
     [Google Scholar]
  22. Kraaijevanger, J.F.B.M., Egberts, P.J.P., Valstar, J.R. and Buurman, H.W.
    [2007] Optimal Waterflood Design Using the Adjoint Method.SPE Reservoir Simulation Symposium, 15. SPE-105764-MS.
     [Google Scholar]
  23. Nwankwor, E., Nagar, A.K. and Reid, D.
    [2013] Hybrid differential evolution and particle swarm optimization for optimal well placement.Computational Geosciences, 17(2), 249–268.
     [Google Scholar]
  24. Pardalos, P.M. and Vavasis, S.A.
    [1991] Quadratic programming with one negative eigenvalue is NP-hard.Journal of Global Optimization, 1(1), 15–22.
     [Google Scholar]
  25. Powell, M.J.D.
    [2004] Least Frobenius norm updating of quadratic models that satisfy interpolation conditions.Mathematical Programming, 100(1), 183–215.
     [Google Scholar]
  26. Sampaio, PR. and Toint, PL.
    [2016] Numerical experience with a derivative-free trust-funnel method for nonlinear optimization problems with general nonlinear constraints.Optimization Methods and Software, 31(3), 511–534.
     [Google Scholar]
  27. Sarma, P., Durlofsky, L.J., Aziz, K. and Chen, W.H.
    [2006] Efficient real-time reservoir management using adjoint-based optimal control and model updating.Computational Geosciences, 10(1), 3–36.
     [Google Scholar]
  28. Scheinberg, K. and Toint, PL.
    [2010] Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization.SIAM Journal on Optimization, 20(6), 3512–3532.
     [Google Scholar]
  29. Silva, T.L., Codas, A., Stanko, M., Camponogara, E., Foss, B. et al.
    [2019] Network-constrained production optimization by means of multiple shooting.SPE Reservoir Evaluation & Engineering.
     [Google Scholar]
  30. Suwartadi, E., Krogstad, S. and Foss, B.
    [2011] Nonlinear output constraints handling for production optimization of oil reservoirs.Computational Geosciences, 16(2), 499–517.
     [Google Scholar]
  31. Volkov, O. and Bellout, M.C.
    [2017] Gradient-based production optimization with simulation-based economic constraints.Computational Geosciences, 21(5), 1385–1402.
     [Google Scholar]
  32. Volkov, O. and Voskov, D.V.
    [2016] Effect of time stepping strategy on adjoint-based production optimization.Computational Geosciences, 20(3), 707–722.
     [Google Scholar]
  33. Wang, C, Li, G. and Reynolds, A.C.
    [2010] Production Optimization in Closed-Loop Reservoir Management.SPE Journal, 14(3), 506–523.
     [Google Scholar]
  34. Yan, X. and Reynolds, A.C.
    [2014] Optimization Algorithms Based on Combining FD Approximations and Stochastic Gradients Compared with Methods Based Only on a Stochastic Gradient.SPE Journal.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202035086
Loading
A Derivative-Free Trust-Region Algorithm for Well Control Optimization
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035086
/content/papers/10.3997/2214-4609.202035086
/content/papers/10.3997/2214-4609.202035086
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error