Source of Publication
The oil and gas industry faces difficulties in optimizing well placement problems. These problems are multimodal, non-convex, and discontinuous in nature. Various traditional and non-traditional optimization algorithms have been developed to resolve these difficulties. Nevertheless, these techniques remain trapped in local optima and provide inconsistent performance for different reservoirs. This study thereby presents a Surrogate Assisted Quantum-behaved Algorithm to obtain a better solution for the well placement optimization problem. The proposed approach utilizes different metaheuristic optimization techniques such as the Quantum-inspired Particle Swarm Optimization and the Quantum-behaved Bat Algorithm in different implementation phases. Two complex reservoirs are used to investigate the performance of the proposed approach. A comparative study is carried out to verify the performance of the proposed approach. The result indicates that the proposed approach provides a better net present value for both complex reservoirs. Furthermore, it solves the problem of inconsistency exhibited in other methods for well placement optimization.
Institute of Electrical and Electronics Engineers (IEEE)
Computer Sciences | Engineering
Optimization, Reservoirs, Oils, Tuning, Metaheuristics, Search problems, Heuristic algorithms
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Islam, Jahedul; Nazir, Amril; Hossain, Moinul; Alhitmi, Hitmi Khalifa; Kabir, Muhammad Ashad; and Jallad, Abdul-Halim, "A Surrogate Assisted Quantum-behaved Algorithm for Well Placement Optimization" (2022). All Works. 4832.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series