A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

IEEE Access

Hitmi Khalifa Alhitmi¹  Abdul-Halim M. Jallad²  Jahedul Islam³  Amril Nazir⁴  Muhammad Ashad Kabir⁵  Md. Moinul Hossain⁶ 

[1] College of Business and Economics, Qatar University, Doha, Qatar;Department of Electrical Engineering, United Arab Emirates University, Al Ain, United Arab Emirates;Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia;Department of Information Systems, College of Technological Innovation, Abu Dhabi Campus, Zayed University, Abu Dhabi, United Arab Emirates;School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia;School of Engineering and Digital Arts, University of Kent, Canterbury, Kent, U.K.;

关键词: Quantum computation;    well placement optimization;    multimodal optimization;    metaheuristic;    nonlinear optimization problem;    reservoir simulation;   

DOI  :  10.1109/ACCESS.2022.3145244

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown