Simultaneous Optimization of Well Placement and Control Using a Hybrid Global-local Strategy

Optimal placement and control of wells is essential to ensuring maximal net present value (NPV) or total oil recovery when developing an oil field. The majority of academic literature treats optimal placement and control as two separate problems; however, treating the problems simultaneously may allow us to achieve better results. The objective function (i.e. NPV) in this joint problem tends to vary nonsmoothly as positional parameters are varied, but smoothly in the control parameters. This suggests an approach that utilizes both global and local optimization techniques. In this paper we address the placement and control optimization problem simultaneously with two approaches combining a global search strategy (particle swarm optimization, or PSO), which operates over all variables, along with a local generalized pattern search (GPS) strategy, which operates primarily on the control parameters. The first approach is a hybrid PSO/GPS algorithm which optimizes over all positional and control variables simultaneously, while the second approach decouples the problem into separate placement and control problems, and attempts to solve them sequentially. Simulation experiments show that both approaches tend to outperform PSO in simple problems, while the decoupled approach may be the most suitable for more complicated cases.