Well Placement Optimization Constrained to Minimum Well Spacing

Abstract

Abstract Efficient recovery of hydrocarbon resources is one of the challenges facing the oil and gas industry. In an enhanced oil recovery (EOR) scheme, optimal placement of wells plays an important role in determining the amount of oil that can be recovered with the selected EOR process. Once the reservoir flow paths have been established, it necessary to find an optimal configuration of wells that will yield the highest net present value (NPV). Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, in recent times, there has been a gradual shift from traditional methods to automatic well placement that uses gradient-based or stochastic search algorithms to locate the optimal positions of wells. This technology has enabled improvement in the decision making process. Despite the successes achieved, the optimization tools rarely enforce well spacing constraints during the optimization process. This often results in well configurations with high NPV but also with physically unrealistic well positions. In this paper, we propose to solve the well placement optimization problem constrained to any desired minimum well spacing. Minimum well spacing here refers to the minimum distance between any two wells that a company or an asset team considers technically safe. First, we develop the nonlinear inequality constraints needed to enforce minimum well spacing, then formulate the well placement problem as a constrained optimization problem and subsequently adopt the penalty approach to solve the constrained well placement problem. The covariance-matrix adaptation evolutionary strategy (CMA-ES) was used as the global optimizer in solving the optimization problem. Two examples were used to show the effectiveness of the approach. Results obtained from this approach were compared with those obtained from the unconstrained WPO. The results show that the method can successfully determine optimal well locations without violating any of the constraints. In contrast, the unconstrained optimization approach failed to satisfy some of the nonlinear constraints and in some cases yielded well configurations in which two or more wells are placed at exactly the same location (on top of each other).