Enhanced Total Organic Carbon Estimation in the Longmaxi Shale Formation: Integrating Unsupervised Clustering with a Stacked Hybrid Machine and Deep Learning Approach

Enhanced Total Organic Carbon Estimation in the Longmaxi Shale Formation: Integrating Unsupervised Clustering with a Stacked Hybrid Machine and Deep Learning Approach | 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 Enhanced Total Organic Carbon Estimation in the Longmaxi Shale Formation: Integrating Unsupervised Clustering with a Stacked Hybrid Machine and Deep Learning Approach Shaukat Khan, Zhishui Liu, Zhiqiang Lu, Wakeel Hussain, Sohail Ahmed, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8009483/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Carbonates and Evaporites → Version 1 posted 13 You are reading this latest preprint version Abstract The Longmaxi formation located within the Sichuan Basin has substantial heterogeneity with respect to its mineral constituents, organic materials, and deposition features. The highly complex internal structures and variations of the formation make the prediction of total organic carbon (TOC) very challenging. This prediction is essential for the evaluation of a shale reservoir. To address this issue, both machine learning (ML) and deep learning (DL) techniques have been applied to several well log data sets with the integration of both supervised and unsupervised learning approaches to enhance and enhance its geological interpretability. Supervised models employed training data consisting of the Uranium (U), bulk density (DEN), Gamma Ray log (GR), Compensated Neutron Log (CNL), Photoelectric Factor log (PE), and Resistivity Deep log (RD) logs, while models that incorporated unsupervised clustering used a subset of the GR, DEN, CNL, and U logs. In supervised modeling, we employed several algorithms, such as Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), Random Forest (RF), and a hybrid stacking model. The performance results were impressively predictive. LightGBM R² values were, respectively, 0.9600 (train) and 0.9238 (test), indicating considerable predictive power, while MLP obtained R² values of 0.9488 (train) and 0.9309 (test). Random Forest had R² values of 0.9677 (train) and 0.8982 (test), and the stacking model, which overall performed best, achieved R² values of 0.9771 (train) and 0.9342 (test). At the same time, K-means clustering was applied and specifically aimed to analyze the Formation’s heterogeneity by assessing the lithological variation at different depths. The results from both the supervised and unsupervised approaches indicate that there is great potential in the use of machine learning and deep learning techniques to enhance the TOC prediction and advance the comprehension of lithological heterogeneity in the Longmaxi Formation of the Sichuan Basin. Hybrid stacking Total Organic Carbon unlabeled data grouping Longmaxi shale Sichuan Basin Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Carbonates and Evaporites → Version 1 posted Editorial decision: Revision requested 27 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviews received at journal 03 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers invited by journal 19 Nov, 2025 Editor assigned by journal 05 Nov, 2025 Submission checks completed at journal 02 Nov, 2025 First submitted to journal 02 Nov, 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-8009483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550235098,"identity":"6ebf943a-1ecd-4847-b95b-ead7268dd7cb","order_by":0,"name":"Shaukat Khan","email":"","orcid":"","institution":"Chang’an University","correspondingAuthor":false,"prefix":"","firstName":"Shaukat","middleName":"","lastName":"Khan","suffix":""},{"id":550235099,"identity":"37455d65-2db1-447f-ac7a-440b4ac394b0","order_by":1,"name":"Zhishui 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Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"carbonates-and-evaporites","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caev","sideBox":"Learn more about [Carbonates and Evaporites](http://link.springer.com/journal/13146)","snPcode":"13146","submissionUrl":"https://submission.nature.com/new-submission/13146/3","title":"Carbonates and Evaporites","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hybrid stacking, Total Organic Carbon, unlabeled data grouping, Longmaxi shale, Sichuan Basin","lastPublishedDoi":"10.21203/rs.3.rs-8009483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8009483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Longmaxi formation located within the Sichuan Basin has substantial heterogeneity with respect to its mineral constituents, organic materials, and deposition features. The highly complex internal structures and variations of the formation make the prediction of total organic carbon (TOC) very challenging. This prediction is essential for the evaluation of a shale reservoir. To address this issue, both machine learning (ML) and deep learning (DL) techniques have been applied to several well log data sets with the integration of both supervised and unsupervised learning approaches to enhance and enhance its geological interpretability. Supervised models employed training data consisting of the Uranium (U), bulk density (DEN), Gamma Ray log (GR), Compensated Neutron Log (CNL), Photoelectric Factor log (PE), and Resistivity Deep log (RD) logs, while models that incorporated unsupervised clustering used a subset of the GR, DEN, CNL, and U logs. In supervised modeling, we employed several algorithms, such as Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), Random Forest (RF), and a hybrid stacking model. The performance results were impressively predictive. LightGBM R\u0026sup2; values were, respectively, 0.9600 (train) and 0.9238 (test), indicating considerable predictive power, while MLP obtained R\u0026sup2; values of 0.9488 (train) and 0.9309 (test). Random Forest had R\u0026sup2; values of 0.9677 (train) and 0.8982 (test), and the stacking model, which overall performed best, achieved R\u0026sup2; values of 0.9771 (train) and 0.9342 (test). At the same time, K-means clustering was applied and specifically aimed to analyze the Formation\u0026rsquo;s heterogeneity by assessing the lithological variation at different depths. The results from both the supervised and unsupervised approaches indicate that there is great potential in the use of machine learning and deep learning techniques to enhance the TOC prediction and advance the comprehension of lithological heterogeneity in the Longmaxi Formation of the Sichuan Basin.\u003c/p\u003e","manuscriptTitle":"Enhanced Total Organic Carbon Estimation in the Longmaxi Shale Formation: Integrating Unsupervised Clustering with a Stacked Hybrid Machine and Deep Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 19:37:16","doi":"10.21203/rs.3.rs-8009483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-28T04:04:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T13:41:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T06:59:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-02T09:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80280769128241887342990106627024244389","date":"2025-11-25T01:09:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T15:14:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152474696910641962874523706255920076828","date":"2025-11-22T09:31:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22534511778695842764292027912738923869","date":"2025-11-20T02:06:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32602177673499667954130427778113750436","date":"2025-11-19T14:22:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T13:23:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T03:56:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-02T11:02:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbonates and Evaporites","date":"2025-11-02T07:31:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"carbonates-and-evaporites","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caev","sideBox":"Learn more about [Carbonates and Evaporites](http://link.springer.com/journal/13146)","snPcode":"13146","submissionUrl":"https://submission.nature.com/new-submission/13146/3","title":"Carbonates and Evaporites","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"92a3dde5-f23d-4edb-8914-4dc620a838bf","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:13:30+00:00","versionOfRecord":{"articleIdentity":"rs-8009483","link":"https://doi.org/10.1007/s13146-026-01266-6","journal":{"identity":"carbonates-and-evaporites","isVorOnly":false,"title":"Carbonates and Evaporites"},"publishedOn":"2026-04-02 15:59:21","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-11-27 19:37:16","video":"","vorDoi":"10.1007/s13146-026-01266-6","vorDoiUrl":"https://doi.org/10.1007/s13146-026-01266-6","workflowStages":[]},"version":"v1","identity":"rs-8009483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8009483","identity":"rs-8009483","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}