A two-stage optimization strategy for large-scale oil field development

Abstract

The optimization of the locations of a large number of wells in an oil or gas field represents a challenging computational problem. This is because the number of optimization variables scales with the maximum number of wells considered. In this work, we develop and test a new two-stage strategy for large-scale oil field optimization problems. In the first stage, wells are constrained to lie in repeated patterns, and the reduced set of optimization variables defines the pattern type and geometry (e.g., well spacing, orientation). For this component of the optimization, we introduce several important modifications, including optimization of the drilling sequence, to an existing well-pattern optimization procedure. The solutions obtained in the first stage are then used to initialize the second stage optimization. In this stage we apply comprehensive field development optimization, where the well location, type (injection or production well), 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. In both stages optimization is achieved using a particle swarm optimization-mesh adaptive direct search (PSO-MADS) method. The two-stage procedure is applied to 2D and 3D models corresponding to different geological scenarios. Both deterministic and geologically uncertain systems are considered. Optimization results using the new procedure are shown to clearly outperform those from the single-stage comprehensive field development optimization approach. Specifically, for the same number of function evaluations, the two-stage treatment provides net present values that exceed those of the single-stage approach by about 15–18% for the cases considered. This suggests that this optimization strategy may indeed lead to improved results in practical problems.