Development of an efficient hybrid ga-pso approach applicable for well placement optimization

Abstract

When it comes to the economic efficiency of oil and gas field development, finding the optimum well locations that augment an economical cost function like net present value is of paramount importance. Well location optimization has long been a challenging problem due to the heterogeneous nature of hydrocarbon reservoirs, economic criteria, and technical uncertainties. These complexities lead to an enormous number of possible solutions that must be evaluated using an evaluation function (e.g., a simulator). This makes it necessary to develop a powerful optimization algorithm into which a fast function evaluation tool is incorporated. The present study describes the application of a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) into a hybrid GA-PSO algorithm that is implemented in a streamline simulator to determine optimal locations for production and injection wells across heterogeneous reservoir models. Performance of the hybrid GA-PSO algorithm is then compared to that of the PSO and the GA separately. The results confirm that compared to conventional methods, the recommended method provides a fast and well-defined approach for production optimization complications.