A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

Autor: Jahedul Islam, Amril Nazir, Md. Moinul Hossain, Hitmi Khalifa Alhitmi, Muhammad Ashad Kabir, Abdul-Halim M. Jallad
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IEEE Access, Vol 10, Pp 17828-17844 (2022)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2022.3145244
Popis: 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.
Databáze: Directory of Open Access Journals