Data-Driven Facies Prediction: A Comparative Study of Random Forest, XGBoost, SVM, CatBoost, and K-Means

Data-Driven Facies Prediction: A Comparative Study of Random Forest, XGBoost, SVM, CatBoost, and K-Means | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Data-Driven Facies Prediction: A Comparative Study of Random Forest, XGBoost, SVM, CatBoost, and K-Means Muhammad Risha, Paul Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6959277/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Facies classification plays a critical role in characterizing subsurface heterogeneity and supporting effective reservoir development. Traditional methods, which often rely on core interpretation and manual log analysis, are limited by subjective interpretation and sparse data coverage. This study aims to improve facies prediction by comparing the performance of five machine learning models: Random Forest, XGBoost, Support Vector Machine, CatBoost, and K-Means clustering. The dataset is derived from sandstone formations in Labuan Island, Malaysia, and is enhanced using synthetic data generated through Latin Hypercube Sampling to address data scarcity. Feature selection is performed using three independent techniques to identify the most informative variables, and Principal Component Analysis is used to investigate feature relationships. Model evaluation is based on classification accuracy, precision-recall metrics, receiver operating characteristic curves, and confusion matrices. Among the models tested, CatBoost achieved the highest cross-validation accuracy at 95.4%, followed by XGBoost at 93.7%. Random Forest achieved a test accuracy of 89.5%, while Support Vector Machine performed less reliably with a test accuracy of 85.6%. The K-Means clustering approach yielded an overall accuracy of 49.7% in aligning predicted clusters with true facies labels. The results demonstrate the effectiveness of ensemble methods in facies classification and support the use of augmented data in enhancing model performance. This approach provides a practical framework for applying machine learning in geological settings, with potential benefits for reservoir modeling and development planning. Facies classification Machine learning Geostatistics Synthetic data augmentation Ensemble models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jul, 2025 Editor assigned by journal 25 Jun, 2025 Submission checks completed at journal 25 Jun, 2025 First submitted to journal 23 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6959277","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":476302622,"identity":"aea510b9-10c3-4b1b-8d82-f33a0b1fc80f","order_by":0,"name":"Muhammad Risha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFACxgZmBgYbKIeNeC1pJGlhYABqOUyCFt3Zh5s/F9Scz+PvP2PA8KHsMGEtZucS26RnHLtdLHEjx4BxxjlitJxhbGPmYbuduEGCx4CZt404Lc2fef6dS9zAf8aA+S+RWhqkedsOJG5gyDFgZiRSS5s0b19y4owbaQUHe86lE6OF/fFnnm92if39hzc++FFmTVgLCjhAovpRMApGwSgYBbgAAIvUOdY2g9ZSAAAAAElFTkSuQmCC","orcid":"","institution":"North Carolina State University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Risha","suffix":""},{"id":476302623,"identity":"6dc428b9-6d90-41f3-901f-a3ac0abb0174","order_by":1,"name":"Paul Liu","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-23 18:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6959277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6959277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86463701,"identity":"872aca79-cf38-4d92-bdda-a5d9fc06ce49","added_by":"auto","created_at":"2025-07-11 02:45:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":837427,"visible":true,"origin":"","legend":"","description":"","filename":"DGRishafaciesmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6959277/v1_covered_f4442c60-8bfc-46aa-bd25-9f924ea87cc0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven Facies Prediction: A Comparative Study of Random Forest, XGBoost, SVM, CatBoost, and K-Means","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Facies classification, Machine learning, Geostatistics, Synthetic data augmentation, Ensemble models","lastPublishedDoi":"10.21203/rs.3.rs-6959277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6959277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFacies classification plays a critical role in characterizing subsurface heterogeneity and supporting effective reservoir development. Traditional methods, which often rely on core interpretation and manual log analysis, are limited by subjective interpretation and sparse data coverage. This study aims to improve facies prediction by comparing the performance of five machine learning models: Random Forest, XGBoost, Support Vector Machine, CatBoost, and K-Means clustering. The dataset is derived from sandstone formations in Labuan Island, Malaysia, and is enhanced using synthetic data generated through Latin Hypercube Sampling to address data scarcity. Feature selection is performed using three independent techniques to identify the most informative variables, and Principal Component Analysis is used to investigate feature relationships. Model evaluation is based on classification accuracy, precision-recall metrics, receiver operating characteristic curves, and confusion matrices. Among the models tested, CatBoost achieved the highest cross-validation accuracy at 95.4%, followed by XGBoost at 93.7%. Random Forest achieved a test accuracy of 89.5%, while Support Vector Machine performed less reliably with a test accuracy of 85.6%. The K-Means clustering approach yielded an overall accuracy of 49.7% in aligning predicted clusters with true facies labels. The results demonstrate the effectiveness of ensemble methods in facies classification and support the use of augmented data in enhancing model performance. 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