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dc.contributor.authorNg, Cuthbert Shang Wui
dc.contributor.authorNait Amar, Menad
dc.contributor.authorJahanbani Ghahfarokhi, Ashkan
dc.contributor.authorImsland, Lars Struen
dc.date.accessioned2022-12-27T13:49:57Z
dc.date.available2022-12-27T13:49:57Z
dc.date.created2022-12-19T09:16:29Z
dc.date.issued2022
dc.identifier.issn0098-1354
dc.identifier.urihttps://hdl.handle.net/11250/3039565
dc.description.abstractMachine Learning (ML) has demonstrated its immense contribution to reservoir engineering, particularly reservoir simulation. The coupling of ML and metaheuristic algorithms illustrates huge potential for application in reservoir simulation, specifically in developing proxy models for fast reservoir simulation and optimization studies. This is conveniently termed the coupled ML-metaheuristic paradigm. Generally, proxy modeling has been extensively researched due to the expensive computational effort needed by traditional Numerical Reservoir Simulation (NRS). ML and the abovementioned coupled paradigm are effective in establishing proxy models. We conduct a survey on the employment of ML and the coupled paradigm in proxy modeling of NRS. We present the respective successful applications as reported in the literature. The benefits and limitations of these methods in intelligent proxy modeling are briefly explained. We opine that some study areas, including sampling techniques and dimensionality reduction methods, are worth investigating as part of the future research development of this technology.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA Survey on the Application of Machine Learning and Metaheuristic Algorithms for Intelligent Proxy Modeling in Reservoir Simulationen_US
dc.title.alternativeA Survey on the Application of Machine Learning and Metaheuristic Algorithms for Intelligent Proxy Modeling in Reservoir Simulationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.journalComputers and Chemical Engineeringen_US
dc.identifier.doihttps://doi.org/10.1016/j.compchemeng.2022.108107
dc.identifier.cristin2094951
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode2


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal