A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

Islam, J; Nazir, A; Hossain, MM; Alhitmi, HK; Kabir, MA; Jallad, AHM

A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization

Islam, J

Nazir, A Hossain, MM Alhitmi, HK Kabir, MA Jallad, AHM

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2022, 10, pp. 17828-17844
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Islam, J https://orcid.org/0000-0002-4077-6589
dc.contributor.author	Nazir, A
dc.contributor.author	Hossain, MM
dc.contributor.author	Alhitmi, HK
dc.contributor.author	Kabir, MA
dc.contributor.author	Jallad, AHM
dc.date.accessioned	2023-03-19T23:19:00Z
dc.date.available	2023-03-19T23:19:00Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Access, 2022, 10, pp. 17828-17844
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/167614
dc.description.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.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2022.3145244
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	A Surrogate Assisted Quantum-Behaved Algorithm for Well Placement Optimization
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-19T23:18:59Z
pubs.publication-status	Published
pubs.volume	10

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.

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http://hdl.handle.net/10453/167614