A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123575512&doi=10.1109%2fACCESS.2022.3145244&partnerID=40&md5=1fae89c79005656db307bace134a8bf8 http://eprints.utp.edu.my/28988/ |
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| Summary: | 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. © 2013 IEEE. |
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