Lithofacies prediction from core images using Bayesian neural networks

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsLithofacies prediction from core images using Bayesian neural networksAuthors: Wei XieJinyu ZhangKyle T. SpikesWei XieThe University of Texas at AustinSearch for more papers by this author, Jinyu ZhangThe University of Texas at AustinSearch for more papers by this author, and Kyle T. SpikesThe University of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3582611.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractMachine learning techniques have gained much interest in reservoir characterization. However, the model prediction often lacks interpretability and reliability due to the ‘black-box’ nature of, for example, neural networks. In this paper, we present a method to identify core lithofacies and measure the uncertainty by U-net based Bayesian Neural Networks. This approach employs variational dropout and maximum a posterior (MAP) estimate, where we can quantify the uncertainties from both the data and model. We applied the method to predict the lithofacies of cores from the Wilcox Group, Gulf of Mexico. A prediction accuracy of 85% was obtained, and the associated uncertainties were evaluated. Although our current database is not complex and large enough to mimic the realworld scenario, it demonstrates the importance of understanding the uncertainties from data and model. Such uncertainties improve the data predictions and enhance the model reliability. They can be employed to quality control the training data and to improve the performance of the trained model.Keywords: machine learning, reservoir characterization, interpretation, neural networks, riskPermalink: https://doi.org/10.1190/segam2021-3582611.1FiguresReferencesRelatedDetails 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 Wei Xie, Jinyu Zhang, and Kyle T. Spikes, (2021), "Lithofacies prediction from core images using Bayesian neural networks," SEG Technical Program Expanded Abstracts : 2134-2138. https://doi.org/10.1190/segam2021-3582611.1 Plain-Language Summary Keywordsmachine learningreservoir characterizationinterpretationneural networksriskPDF DownloadLoading ...