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- W3197564742 abstract "PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsA random forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributesAuthors: Caifeng ZouLuanxiao ZhaoMinghui XuYuanyuan ChenJianhua GengCaifeng ZouTongji UniversitySearch for more papers by this author, Luanxiao ZhaoTongji UniversitySearch for more papers by this author, Minghui XuTongji UniversitySearch for more papers by this author, Yuanyuan ChenTongji UniversitySearch for more papers by this author, and Jianhua GengTongji UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3582868.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractInferring subsurface porosity from seismic data is of profound significance to hydrocarbon reservoir characterization. Traditional model-driven approaches confront the problems of strong nonlinearity and geological heterogeneity, while machine learning is good at nonlinear mapping, providing higher efficiency as well. We propose a Random Forest (RF) regressor based method using multiple seismic attributes to predict the porosity distribution with uncertainty quantification. The standard deviation of base models’ predictions is used to quantify the regression uncertainty of RF. The uncertainty can robustly indicate the prediction quality, where low uncertainty corresponds to relatively precise prediction and high uncertainty gives a possibility of bigger errors. Furthermore, we utilize the quantified uncertainty to improve the RF regression accuracy by correcting the original predicted porosity according to the statistical relationship between the absolute error and the standard deviation. The application of the proposed method on seismic data reaches a more geologically reasonable porosity distribution, and the quantified uncertainty profile offers insights into risk evaluation for hydrocarbon exploration and development.Keywords: machine learning, porosity, seismic attributes, reservoir characterization, clasticPermalink: https://doi.org/10.1190/segam2021-3582868.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Caifeng Zou, Luanxiao Zhao, Minghui Xu, Yuanyuan Chen, and Jianhua Geng, (2021), A random forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributes, SEG Technical Program Expanded Abstracts : 1606-1610. https://doi.org/10.1190/segam2021-3582868.1 Plain-Language Summary Keywordsmachine learningporosityseismic attributesreservoir characterizationclasticPDF DownloadLoading ..." @default.
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- W3197564742 title "A random forest regressor based uncertainty quantification of porosity estimation from multiple seismic attributes" @default.
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