1887

Abstract

Summary

The optimization of the locations of a large number of wells represents a challenging computational problem. This is because the number of optimization variables scales with the maximum number of wells considered, and some of these variables may be categorical if the determination of the number and types of wells is part of the optimization problem. In this work, we develop and test a two-stage strategy for large-scale field development optimization problems. In the first stage, wells are constrained to lie in repeated patterns, and the optimization variables define the pattern type and geometry (e.g., well spacing, orientation). This component of the optimization follows a previous procedure ( ), though several important modifications, including optimization of the drilling sequence, are introduced. The solution obtained in the first stage is used as an initial guess for the second stage. In this stage we apply comprehensive field development optimization, where the well location, type, drill/do not drill decision, completion interval (for 3D models), and drilling time variables are determined for each well. Pattern geometry is no longer enforced in this stage. Specialized treatments (consistent with actual drilling practice) are introduced for cases where multiple geomodels, used to capture geological uncertainty, are considered.

The two-stage procedure is applied to 2D and 3D models corresponding to different geological scenarios. Both deterministic and geologically uncertain settings are considered. All optimizations are performed using a derivative-free particle swarm optimization – mesh adaptive direct search hybrid algorithm. Our most challenging example involves optimization over multiple realizations of the Olympus model, which we simulate using a GPU- based commercial flow simulator. In all cases, results using the two-stage procedure are compared to those from a standard single-stage approach. We achieve consistently better optimizer performance using the two-stage approach. For example, in one case, the optimum achieved after 17,500 flow simulations using the standard approach is found after only 4400 flow simulations using the two-stage approach. In another case, for the same computational effort, the NPV achieved using the two-stage approach exceeds that of the standard approach by 4.7%. These results suggest that this optimization strategy may indeed lead to improved results in practical problems.

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/content/papers/10.3997/2214-4609.202035030
2020-09-14
2021-09-20
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References

  1. Aliyev, E. and Durlofsky, L.J.
    [2017] Multilevel field development optimization under uncertainty using a sequence of upscaled models. Mathematical Geosciences, 49(3), 307–339.
    [Google Scholar]
  2. Awotunde, A.A.
    [2014] On the joint optimization of well placement and control. Paper SPE-172206-MS presented at the SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 21–24 April.
    [Google Scholar]
  3. [2019] A comprehensive evaluation of dimension-reduction approaches in optimization of well rates. SPE Journal, 24(03), 912–950.
    [Google Scholar]
  4. de Brito, D.U. and Durlofsky, L.J.
    [2020] Well control optimization using a two-step surrogate treatment. Journal of Petroleum Science and Engineering, 187, 106565.
    [Google Scholar]
  5. Cardoso, M.A. and Durlofsky, L.J.
    [2010] Linearized reduced-order models for subsurface flow simulation. Journal of Computational Physics, 229(3), 681–700.
    [Google Scholar]
  6. Echelon
    Echelon [2019] Echelon User’s Guide v2019.3. Stone Ridge Technology.
    [Google Scholar]
  7. Fan, Z., Cheng, L., Yang, D. and Li, X.
    [2018] Optimization of well pattern parameters for waterflooding in an anisotropic formation. Mathematical Geosciences, 50(8), 977–1002.
    [Google Scholar]
  8. Fonseca, R., Della Rossa, E., 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, 2018. European Association of Geoscientists & Engineers.
    [Google Scholar]
  9. Fu, J. and Wen, X.
    [2018] A regularized production-optimization method for improved reservoir management. SPE Journal, 23(02), 467–481.
    [Google Scholar]
  10. Guo, Z. and Reynolds, A.C.
    [2018] Robust life-cycle production optimization with a support-vector-regression proxy. SPE Journal, 23(06), 2–409.
    [Google Scholar]
  11. He, J., Sætrom, J. and Durlofsky, L.J.
    [2011] Enhanced linearized reduced-order models for subsurface flow simulation. Journal of Computational Physics, 230(23), 8313–8341.
    [Google Scholar]
  12. Isebor, O.J., Durlofsky, L.J. and Echeverría Ciaurri, D.
    [2014a] A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls. Computational Geosciences, 18(3û4), 463–482.
    [Google Scholar]
  13. Isebor, O.J., Echeverría Ciaurri, D. and Durlofsky, L.J.
    [2014b] Generalized field-development optimization with derivative-free procedures. SPE Journal, 19(05), 891–908.
    [Google Scholar]
  14. Jansen, J.D. and Durlofsky, L.J.
    [2017] Use of reduced-order models in well control optimization. Optimization and Engineering, 18(1), 105–132.
    [Google Scholar]
  15. Jin, Z.L., Liu, Y. and Durlofsky, L.J.
    [2020] Deep-learning-based surrogate model for reservoir simulation with time-varying well controls. Journal of Petroleum Science and Engineering, 192, 107273.
    [Google Scholar]
  16. Litvak, M.L. and Angert, P.F.
    [2009] Field development optimization applied to giant oil fields. Paper SPE-118840-MS presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2–4 February.
    [Google Scholar]
  17. Liu, Y., Sun, W. and Durlofsky, L.J.
    [2019] A deep-learning-based geological parameterization for history matching complex models. Mathematical Geosciences, 51(6), 725–766.
    [Google Scholar]
  18. Møyner, O., Krogstad, S. and Lie, K.A.
    [2015] The application of flow diagnostics for reservoir management. SPE Journal, 20(2), 306–323.
    [Google Scholar]
  19. Nasir, Y., Yu, W. and Sepehrnoori, K.
    [2020] Hybrid derivative-free technique and effective machine learning surrogate for nonlinear constrained well placement and production optimization. Journal of Petroleum Science and Engineering, 186, 106726.
    [Google Scholar]
  20. Onwunalu, J.E. and Durlofsky, L.J.
    [2010] Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 14(1), 183–198.
    [Google Scholar]
  21. [2011] A new well-pattern-optimization procedure for large-scale field development. SPE Journal, 16(03), 594–607.
    [Google Scholar]
  22. Ozdogan, U., Sahni, A., Yeten, B., Guyaguler, B. and Chen, W.H.
    [2005] Efficient assessment and optimization of a deepwater asset development using fixed pattern approach. Paper SPE-95792-MS presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, 9–12 October.
    [Google Scholar]
  23. Pan, Y. and Horne, R.
    [1998] Improved methods for multivariate optimization of field development scheduling and well placement design. Paper SPE-49055-MS presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 27–30 September.
    [Google Scholar]
  24. Shirangi, M.G. and Durlofsky, L.J.
    [2015] Closed-loop field development under uncertainty by use of optimization with sample validation. SPE Journal, 20(05), 908–922.
    [Google Scholar]
  25. Sorek, N., Gildin, E., Boukouvala, F., Beykal, B. and Floudas, C.A.
    [2017] Dimensionality reduction for production optimization using polynomial approximations. Computational Geosciences, 21(2), 247–266.
    [Google Scholar]
  26. Van Doren, J.F., Markovinović, R. and Jansen, J.D.
    [2006] Reduced-order optimal control of water flooding using proper orthogonal decomposition. Computational Geosciences, 10(1), 137–158.
    [Google Scholar]
  27. Volz, R., Burn, K., Litvak, M., Thakur, S. and Skvortsov, S.
    [2008] Field development optimization of Siberian giant oil field under uncertainties. Paper SPE-116831-MS presented at the SPE Russian Oil and Gas Technical Conference and Exhibition, Moscow, Russia, 28–30 October.
    [Google Scholar]
  28. Yeten, B., Wolfsteiner, C., Durlofsky, L.J. and Aziz, K.
    [2000] Approximate finite difference modeling of the performance of horizontal wells in heterogeneous reservoirs. Paper SPE-62555-MS presented at the SPE/AAPG Western Regional Meeting, Long Beach, California, 19–23 June.
    [Google Scholar]
  29. Zhang, K., Zhang, H., Zhang, L., Li, P., Zhang, X. and Yao, J.
    [2017] A new method for the construction and optimization of quadrangular adaptive well pattern. Computational Geosciences, 21(3), 499–518.
    [Google Scholar]
  30. Zhou, Y.
    [2012] Parallel general-purpose reservoir simulation with coupled reservoir models and multisegment wells. Ph.D. thesis, Stanford University.
    [Google Scholar]
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