Generalized Field-Development Optimization With Derivative-Free Procedures
- Obiajulu J. Isebor (BP) | David Echeverría Ciaurri (IBM) | Louis J. Durlofsky (Stanford University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- October 2014
- Document Type
- Journal Paper
- 891 - 908
- 2014.Society of Petroleum Engineers
- 1.6 Drilling Operations, 1.6.9 Coring, Fishing
- simulation-based optimization, mixed-integer nonlinear programming, field development optimization, derivative-free optimization
- 7 in the last 30 days
- 573 since 2007
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The optimization of general oilfield development problems is considered. Techniques are presented to simultaneously determine the optimal number and type of new wells, the sequence in which they should be drilled, and their corresponding locations and (time-varying) controls. The optimization is posed as a mixed-integer nonlinear programming (MINLP) problem and involves categorical, integer-valued, and real-valued variables. The formulation handles bound, linear, and nonlinear constraints, with the latter treated with filter-based techniques. Noninvasive derivative-free approaches are applied for the optimizations. Methods considered include branch and bound (B&B), a rigorous global-search procedure that requires relaxation of the categorical variables; mesh adaptive direct search (MADS), a local pattern-search method; particle swarm optimization (PSO), a heuristic global-search method; and a PSO-MADS hybrid. Four example cases involving channelized-reservoir models are presented. The recently developed PSO-MADS hybrid is shown to consistently outperform the standalone MADS and PSO procedures. In the two cases in which B&B is applied, the heuristic PSO-MADS approach is shown to give comparable solutions but at much lower computational cost. This is significant since B&B provides a systematic search in the categorical variables. We conclude that, although it is demanding in terms of computation, the methodology presented here, with PSO-MADS as the core optimization method, appears to be applicable for realistic reservoir development and management.
|File Size||1 MB||Number of Pages||18|
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