Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91953
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorNourani, Men_US
dc.creatorAlali, Nen_US
dc.creatorSamadianfard, Sen_US
dc.creatorBand, SSen_US
dc.creatorChau, KWen_US
dc.creatorShu, CMen_US
dc.date.accessioned2022-02-04T08:03:02Z-
dc.date.available2022-02-04T08:03:02Z-
dc.identifier.issn0920-4105en_US
dc.identifier.urihttp://hdl.handle.net/10397/91953-
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.rights© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Nourani, M., Alali, N., Samadianfard, S., Band, S. S., Chau, K.-w., & Shu, C.-M. (2022). Comparison of machine learning techniques for predicting porosity of chalk. Journal of Petroleum Science and Engineering, 209, 109853 is available at https://dx.doi.org/10.1016/j.petrol.2021.109853.en_US
dc.subjectPorosityen_US
dc.subjectChalken_US
dc.subjectHand-held X-ray fluorescenceen_US
dc.subjectRandom foresten_US
dc.subjectMultilayer perceptronen_US
dc.subjectRandom forest optimized by genetic algorithmen_US
dc.subjectMultilayer perceptron optimized by genetic algorithmen_US
dc.titleComparison of machine learning techniques for predicting porosity of chalken_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume209en_US
dc.identifier.doi10.1016/j.petrol.2021.109853en_US
dcterms.abstractPrecise and fast estimation of porosity is a vital element of reservoir characterization. A new technology for fast and reliable porosity prediction of chalk samples is presented by applying machine learning methods and X-ray fluorescence (XRF) elemental analysis. Input parameters of prediction models are based on rapid and accurate elemental analysis of chalk samples obtained from Hand-held X-ray fluorescence (HH-XRF) measurements. The intelligent models, including Random Forest (RF), Multilayer perceptron (MLP), Random Forest integrated by Genetic Algorithm (GA-RF) and Multilayer Perceptron integrated by Genetic Algorithm (GA-MLP), are trained and tested based on samples consisting of outcrop chalk samples from Rørdal and Stevns Klint (ST) and core samples from Ekofisk Formation in the North Sea. Results are evaluated by sustainability index (SI), determination coefficient (R2), correlation coefficient (CC), and Willmott's Index of agreement (WI). Results indicate that the combination of GA-RF intelligent method with XRF elemental analysis successfully provides an accurate model by 0.99, 0.02, 0.995 and 0.99 respectively for CC, SI, WI and R2, respectively.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of petroleum science and engineering, Feb. 2022, v. 209, 109853en_US
dcterms.isPartOfJournal of petroleum science and engineeringen_US
dcterms.issued2022-02-
dc.identifier.isiWOS:000741667900002-
dc.identifier.scopus2-s2.0-85120879743-
dc.identifier.eissn1873-4715en_US
dc.identifier.artn109853en_US
dc.description.validate202202 bcvcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera1153-n01-
dc.identifier.SubFormID44017-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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