A new workflow for multi-well lithofacies interpretation integrating joint petrophysical inversion, unsupervised, and supervised machine learning

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsA new workflow for multi-well lithofacies interpretation integrating joint petrophysical inversion, unsupervised, and supervised machine learningAuthors: Sumit VermaShuvajit BhattacharyaNur Uddin Md Khaled ChowdhuryMiao TianSumit VermaThe University of Texas Permian BasinSearch for more papers by this author, Shuvajit BhattacharyaThe University of Texas at AustinSearch for more papers by this author, Nur Uddin Md Khaled ChowdhuryThe University of Texas Permian BasinSearch for more papers by this author, and Miao TianThe University of Texas Permian BasinSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3584118.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractLithofacies identification is one of the first steps for subsurface characterization and 3D geological modeling. We present an integrated workflow for facies identification using petrophysical inversion, unsupervised and supervised machine learning. We applied it in the Wolfcamp Formation of the Permian Basin, United States. In our workflow, we perform a joint petrophysical inversion to derive multi-mineral solution calibrated with core measurement. We apply the commonly used cut-off-based method on petrophysically derived mineralogy to generate geologically meaningful lithofacies. Cut-off based method has limitations since cut-off values follow a linear trend; it does not capture the complexities. So, we apply an unsupervised facies classification on the calibrated mineral volumes using the self-organizing map method (SOM) with multiple models. In order to obtain geologically meaningful facies, we calibrate SOM models with mineralogical facies. Further, we use the calibrated facies as ground truth and predict it using five different supervised machine learning approaches including traditional and deep learning methods. Support vector machine (SVM), random forest (RF), neural network with multilayer perception (NN), convolutional neural network (CNN), and long-short term memory (LSTM) resulted in correlations of 73%, 72%, 70%, 78%, and 80%, respectively on a blind test well.Keywords: facies, machine learning, petrophysics, borehole geophysics, unconventionalPermalink: https://doi.org/10.1190/segam2021-3584118.1FiguresReferencesRelatedDetails 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 Sumit Verma, Shuvajit Bhattacharya, Nur Uddin Md Khaled Chowdhury, and Miao Tian, (2021), "A new workflow for multi-well lithofacies interpretation integrating joint petrophysical inversion, unsupervised, and supervised machine learning," SEG Technical Program Expanded Abstracts : 2213-2217. https://doi.org/10.1190/segam2021-3584118.1 Plain-Language Summary Keywordsfaciesmachine learningpetrophysicsborehole geophysicsunconventionalPDF DownloadLoading ...