The optimization of general oil field 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; the latter are treated using 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. Three example cases involving two-dimensional channelized reservoir models are presented. The 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 exhaustive search capabilities. We conclude that, although it is demanding in terms of computation, the methodology presented here appears to be applicable for realistic reservoir management.
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