Applying an ensemble support vector machine (SVM) to lithofacies prediction in the Lower Huron Shale

PreviousNext No AccessSEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019Applying an ensemble support vector machine (SVM) to lithofacies prediction in the Lower Huron ShaleAuthors: Yue Hu *Ellen GillilandJinglin PengNino RipepiYue Hu *Virginia Polytechnic Institute and State UniversitySearch for more papers by this author, Ellen GillilandVirginia Polytechnic Institute and State UniversitySearch for more papers by this author, Jinglin PengSimon Fraser UniversitySearch for more papers by this author, and Nino RipepiVirginia Polytechnic Institute and State UniversitySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2019_30.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Shale lithofacies, defined by the amount of total organic carbon (TOC) and minerals composition, are valuable in identifying reservoir “sweet spots.” A study of the Lower Huron shale in the Appalachian Basin will apply an ensemble support vector machine (SVM) to classify shale lithofacies from existing well data. The method breaks down the multiple classification problem into several binary sub-classification tasks. Existing data include a limited amount of shale lithofacies interpreted directly from core tests and advanced well logging. Data from conventional log suites will be trained in each sub-classification to predict the lithofacies. The prediction results will be combined and validated in the ensemble process using the bootstrap aggregation (bagging) method to improve model accuracy. Keywords: machine learning, shale, logging, wells, faciesPermalink: https://doi.org/10.1190/iwmg2019_30.1FiguresReferencesRelatedDetails SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019ISSN (online):2159-6832Copyright: 2020 Pages: 138 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 17 Jan 2020 CITATION INFORMATION Yue Hu *, Ellen Gilliland, Jinglin Peng, and Nino Ripepi, (2020), "Applying an ensemble support vector machine (SVM) to lithofacies prediction in the Lower Huron Shale," SEG Global Meeting Abstracts : 123-126. https://doi.org/10.1190/iwmg2019_30.1 Plain-Language Summary Keywordsmachine learningshaleloggingwellsfaciesPDF DownloadLoading ...