A time-bounded approach for reducing runtime of well placement optimization using PSO and PSOGA algorithms

Abstract

Determining the optimal drilling locations and well production/injection rates is a highly complex decision in reservoir development. The present study aims to optimize well placement and production/injection rates through the application of intelligent search algorithms. The Particle Swarm Optimization (PSO) and combined Particle Swarm Optimization-Genetic Algorithm (PSOGA) have been chosen as the optimal means to achieve this objective. The Net Present Value (NPV) of the project serves as the objective function, with decision variables including the location of wells, perforation, and flow rates. To evaluate the performance of the algorithms in optimizing well placement and flow rates, a heterogeneous reservoir model has been used. In order to reduce optimization runtime, a novel method has been presented. This method involves changing the termination condition, such that the entire optimization time is considered rather than the maximum iterations. The population size can be added to enhance algorithm convergence. Thus, modified algorithms, namely TPSO and TPSO-GA, have been introduced. The results of determining the termination condition on the TPSO-GA algorithm are quite promising. By setting a 24-h termination condition or a 70% reduction in the basic runtime, and with a population size of 30 agents, the same values of the objective function can be obtained. Furthermore, the impact of two parameters within the objective function, namely the oil price and discount rate, has also been explored.