Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space
Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the availabl...
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| Veröffentlicht in: | PloS one 2023-02, Vol.18 (2), p.e0282084-e0282084 |
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| creator | Li, Daihong Zhang, Xiaoyu Kang, Qian |
| description | Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity. |
| doi_str_mv | 10.1371/journal.pone.0282084 |
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Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0282084</identifier><identifier>PMID: 36800383</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Analysis ; Biology and Life Sciences ; Chemical properties ; Composition ; Computer and Information Sciences ; Computer applications ; Computer simulation ; Computer-generated environments ; Crude oil ; Datasets ; Decision making ; Decision trees ; Design optimization ; Engineering and Technology ; Estimation ; Flight simulators ; Health aspects ; Heavy petroleum ; Heuristic methods ; Learning algorithms ; Machine Learning ; Mathematical models ; Methods ; Modelling ; Neural networks ; Oil and Gas Fields ; Oil fields ; Oils & fats ; Optimization ; Performance evaluation ; Petroleum ; Physical Sciences ; Process parameters ; Research and Analysis Methods ; Reservoirs ; Root-mean-square errors ; Statistical analysis ; Temperature ; Variables ; Viscosity ; Viscosity measurement</subject><ispartof>PloS one, 2023-02, Vol.18 (2), p.e0282084-e0282084</ispartof><rights>Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Li et al 2023 Li et al</rights><rights>2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a645t-1f9c7af197918df2da87517d2015745dc51797492630b9540daf68c58a14d7493</citedby><cites>FETCH-LOGICAL-a645t-1f9c7af197918df2da87517d2015745dc51797492630b9540daf68c58a14d7493</cites><orcidid>0000-0001-5646-8663</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937493/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937493/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36800383$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Peng, Lu</contributor><creatorcontrib>Li, Daihong</creatorcontrib><creatorcontrib>Zhang, Xiaoyu</creatorcontrib><creatorcontrib>Kang, Qian</creatorcontrib><title>Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Chemical properties</subject><subject>Composition</subject><subject>Computer and Information Sciences</subject><subject>Computer applications</subject><subject>Computer simulation</subject><subject>Computer-generated environments</subject><subject>Crude oil</subject><subject>Datasets</subject><subject>Decision making</subject><subject>Decision trees</subject><subject>Design optimization</subject><subject>Engineering and Technology</subject><subject>Estimation</subject><subject>Flight simulators</subject><subject>Health aspects</subject><subject>Heavy petroleum</subject><subject>Heuristic methods</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Mathematical 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estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-02-17</date><risdate>2023</risdate><volume>18</volume><issue>2</issue><spage>e0282084</spage><epage>e0282084</epage><pages>e0282084-e0282084</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36800383</pmid><doi>10.1371/journal.pone.0282084</doi><tpages>e0282084</tpages><orcidid>https://orcid.org/0000-0001-5646-8663</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Algorithms Analysis Biology and Life Sciences Chemical properties Composition Computer and Information Sciences Computer applications Computer simulation Computer-generated environments Crude oil Datasets Decision making Decision trees Design optimization Engineering and Technology Estimation Flight simulators Health aspects Heavy petroleum Heuristic methods Learning algorithms Machine Learning Mathematical models Methods Modelling Neural networks Oil and Gas Fields Oil fields Oils & fats Optimization Performance evaluation Petroleum Physical Sciences Process parameters Research and Analysis Methods Reservoirs Root-mean-square errors Statistical analysis Temperature Variables Viscosity Viscosity measurement |
| title | Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space |
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