Optimization of Well Placement in a Gulf of Mexico Waterflooding Project

Summary In this study, a hybrid optimization technique based on the genetic algorithm (GA), polytope algorithm, kriging algorithm, and neural networks is proposed to optimize a waterflooding project. Hybridization of the GA with these helper methods introduces hill climbing into the stochastic search and makes use of proxies created on the fly. It was observed that the number of simulations required was reduced significantly, as compared to conventional approaches. This reduction in the number of simulations reduced the computation time, enabling the use of full-scale simulation for optimization even for this full-scale field problem. It was also seen that the optimization technique was able to prevent convergence to local maxima owing to its stochastic nature.