A new hybrid rate optimization method to enhance oil recovery from Brugge field using streamline-driven injection efficiencies

Abstract

BackgroundOptimization of waterflooding projects traditionally focuses on optimizing rate control of injector/producer wells to enhance recovered oil from reservoirs. In such projects, a complex, constrained, multi-variable problem is optimized to enhance the oil recovery performance of reservoirs. MethodsStreamline-based rate optimization has been applied as a robust method to find the optimal water injection strategy. The present study aims to optimize injector/producer wells rates using streamline-driven injection efficiencies. A heuristic algorithm (the so-called weights algorithm) was used in previous works. This study proposes a new hybrid particle swarm optimization (PSO)-weights method. The weights algorithm oil recovery results are dependent on the initial guesses to start searching the domain. PSO global search ability provides a proper set of initial guesses for the weights algorithm. Significant findingsThe hybrid method is implemented to optimize two reservoir models. First, the PSO-weights algorithm is applied on a 2-D homogenous model with 13 wells; after optimization, injectors/producers’ computed rate converged to known optimal rates. Second, the proposed algorithm has been tested to optimize the Brugge field and successfully afforded it quickly. The field oil production rate (FOPR) and the total field oil production (FOPT) results indicate an improvement in oil production. In addition, less trapped oil in saturation contours in different reservoir layers is reported.