Derivative-Free Optimization for Generalized Oil Field Development

Advisors

Louis Durlofsky, primary advisor
Khalid Aziz, advisor
Tapan Mukerji, advisor

Abstract

Given the substantial costs and potential rewards associated with petroleum field development and reservoir management, it is essential that these operations be performed as close to optimally as possible. This work presents numerical methods for field development optimization where the goal is to simultaneously determine the optimal number and type of new wells, the sequence in which they should be drilled, as well as 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 Mesh Adaptive Direct Search (MADS, a local pattern search method), Particle Swarm Optimization (PSO, a heuristic global search method) and a PSO-MADS hybrid. Single and biobjective optimization example cases are presented. These cases involve well control optimization, joint well placement and control, and generalized full-field development problems. Significant improvement over base-case field development plans is demonstrated in all cases and the PSO-MADS hybrid procedure is shown to outperform its component methods. It is concluded that, although demanding in terms of computation, the methodology presented here is applicable for realistic petroleum field development and reservoir management.