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dc.contributor.authorNg, Cuthbert Shang Wui
dc.contributor.authorJahanbani Ghahfarokhi, Ashkan
dc.contributor.authorNait Amar, Menad
dc.date.accessioned2022-03-08T10:24:26Z
dc.date.available2022-03-08T10:24:26Z
dc.date.created2021-06-24T20:11:47Z
dc.date.issued2021
dc.identifier.citationJournal of Petroleum Exploration and Production Technology. 2021, 11 3103-3127.en_US
dc.identifier.issn2190-0558
dc.identifier.urihttps://hdl.handle.net/11250/2983735
dc.description.abstractWith the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R2 being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleApplication of nature-inspired algorithms and artificial neural network in waterflooding well control optimizationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber3103-3127en_US
dc.source.volume11en_US
dc.source.journalJournal of Petroleum Exploration and Production Technologyen_US
dc.identifier.doi10.1007/s13202-021-01199-x
dc.identifier.cristin1918291
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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