[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127529-en":3,"doc-seo-127529-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127529,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Accuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin","Litho-facies classification underpins characterization of complex petroleum reservoirs and supports later field development. Borehole-based litho-facies mapping is detailed but limited to well locations, while 3D post-stack seismic data offers wider coverage for studying facies heterogeneity between wells. This study applies six supervised machine learning methods—Random Forest, SVM, ANN, Adaptive Boosting, XGBoost, and Multilayer Perceptron—to seismic attributes to estimate litho-facies in inter-well regions. Performance is evaluated using confusion matrices and metrics including precision, recall, and F1-score. Shale facies achieves the highest accuracies, and multilayer perceptron ranks best overall. Predicted facies align with log-interpreted facies at wells, and results are further validated with additional seismic attributes, supporting accurate, reliable inter-well estimation.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10.3389/feart.2023.1150954  \nOPEN ACCESS  \nEDITED BY  \nAydın Büyüksaraç,  \nÇanakkale Onsekiz Mart University, Türkiye  \nREVIEWED BY  \nHakan Karslı,  \nKaradeniz Technical University, Türkiye Sayed Elkhateeb,  \nSouth Valley University, Egypt  \n*CORRESPONDENCE  \nAhmed M. Eldosouky,  \n [dr_a.eldosoky@yahoo.com](dr_a.eldosoky@yahoo.com)  \nRECEIVED 25 January 2023  \nACCEPTED 25 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nNarayan S, Konka S, Chandra A, Abdelrahman K, Andráš P and Eldosouky AM (2023), Accuracy assessment of various supervised machine learning algorithms in lithofacies classification from seismic data in the Penobscot field, Scotian Basin. Front. Earth Sci. 11:1150954 .  \ndoi: 10.3389/feart.2023.1150954  \nCOPYRIGHT  \n© 2023 Narayan, Konka, Chandra, Abdelrahman, Andráš and Eldosouky. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nAccuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin  \nSatya Narayan 1, Suresh Konka 1, Akash Chandra 1, Kamal Abdelrahman 2, Peter Andráš3 and Ahmed M. Eldosouky 􀀁 4*  \n1Oil and Natural Gas Corporation (ONGC), Dehradun, India, 2Department of Geology and Geophysics, College of Science, King Saud University, Riyadh, Saudi Arabia, 3Faculty of Natural Sciences, Matej Bel University in Banska Bystrica, Banska Bystrica, Slovakia, 4Geology Department, Faculty of Science, Suez University, Suez, Egypt  \nLitho-facies classification is an essential task in characterizing the complex reservoirs in petroleum exploration and subsequent field development. The lithofacies classification at borehole locations is detailed but lacks in providing larger coverage areas. The acquired 3D seismic data provides global coverage for studying the reservoir facies heterogeneities in the study area. This study applies six supervised machine learning techniques (Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting, and Multilayer Perceptron) to 3D post-stack seismic data to accurately estimate different litho-facies in inter-well regions and compares their performance. Initially, the efficacy of the said models was critically examined via the confusion matrix (accuracy and misclass) and evaluation matrix (precision, recall, F1-score) on the test data. It was found that all the machine learning models performed best in classifying the shale facies (87%–94%) followed by the sand (65%–79%) and carbonate facies (60%–78%) in the Penobscot field, Scotian Basin. On an overall accuracy scale, we found the multilayer perceptron method the best-performing tool, whereas the adaptive boosting method was the least-performing tool in classifying all three litho-facies in the current analysis. While other methods also performed moderately good for the classification of all three litho-facies. The predicted litho-facies using seismic attributes matched well with the log data interpreted facies on the borehole locations. It indicates that the facies estimated in inter-well regions are accurate and reliable. Furthermore, we validated the estimated results with the other seismic attributes to ascertain the accuracy and reliability of the predicted litho-facies between the borehole locations. This study recommends machine learning applications for litho-facies classification to reduce the risk associated with reservoir characterization.  \nKEYWORDS  \nmachine learning, litho-facies classification, validation, hydrocarbon exploration, Penobscot field ","cbCaiff2D9ELe89u","https://ap.wps.com/l/cbCaiff2D9ELe89u","pdf",6599166,2,1,14,"English","en",105,"# 1 Introduction","[{\"question\":\"What problem does the study address in litho-facies classification?\",\"answer\":\"It addresses the gap that borehole-based litho-facies detail is limited to well positions, motivating inter-well prediction using widely covering seismic data.\"},{\"question\":\"Which supervised machine learning algorithms are used?\",\"answer\":\"Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting (XGBoost), and Multilayer Perceptron are applied to 3D post-stack seismic data.\"},{\"question\":\"How is model performance evaluated and validated?\",\"answer\":\"Performance is assessed via confusion matrices and evaluation metrics such as precision, recall, and F1-score on test data, and predicted facies are checked against log-interpreted facies at boreholes and validated using additional seismic attributes.\"}]","Accuracy assessment of various supervised machine learning algorithms in litho-facies classification from seismic data in the Penobscot field, Scotian Basin | 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problem does the study address in litho-facies classification?","Question",{"text":76,"@type":77},"It addresses the gap that borehole-based litho-facies detail is limited to well positions, motivating inter-well prediction using widely covering seismic data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which supervised machine learning algorithms are used?",{"text":81,"@type":77},"Random Forest, Support Vector Machine, Artificial Neural Network, Adaptive Boosting, Xtreme Gradient Boosting (XGBoost), and Multilayer Perceptron are applied to 3D post-stack seismic data.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated and validated?",{"text":85,"@type":77},"Performance is assessed via confusion matrices and evaluation metrics such as precision, recall, and F1-score on test data, and predicted facies are checked against log-interpreted facies at boreholes and validated using additional seismic 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