Resumen
The high dimensional, multimodal, and discontinuous well placement optimization is one of the main difficult factors in the development process of conventional as well as shale gas reservoir, and to optimize this problem, metaheuristic techniques still suffer from premature convergence. Hence, to tackle this problem, this study aims at introducing a dimension-wise diversity analysis for well placement optimization. Moreover, in this article, quantum computational techniques are proposed to tackle the well placement optimization problem. Diversity analysis reveals that dynamic exploration and exploitation strategy is required for each reservoir. In case studies, the results of the proposed approach outperformed all the state-of-the-art algorithms and provided a better solution than other algorithms with higher convergence rate, efficiency, and effectiveness. Furthermore, statistical analysis shows that there is no statistical difference between the performance of Quantum bat algorithm and Quantum Particle swarm optimization algorithm. Hence, this quantum adaptation is the main factor that enhances the results of the optimization algorithm and the approach can be applied to locate wells in conventional and shale gas reservoir.