@article{scholars17770,
            year = {2022},
         journal = {IEEE Access},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           pages = {17828--17844},
          volume = {10},
            note = {cited By 4},
             doi = {10.1109/ACCESS.2022.3145244},
           title = {A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization},
          author = {Islam, J. and Nazir, A. and Hossain, M. M. and Alhitmi, H. K. and Kabir, M. A. and Jallad, A.-H. M.},
            issn = {21693536},
        abstract = {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. {\^A}{\copyright} 2013 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123575512&doi=10.1109\%2fACCESS.2022.3145244&partnerID=40&md5=1fae89c79005656db307bace134a8bf8},
        keywords = {Gas industry; Heuristic algorithms; Nonlinear programming; Petroleum reservoirs; Quantum computers, Heuristics algorithm; Metaheuristic; Multi-modal optimization; Nonlinear optimization problems; Oil; Optimisations; Reservoir-simulation; Search problem; Tuning; Well placement optimization, Particle swarm optimization (PSO)}
}