Machine Learning-Driven Subsurface Zonation and Connectivity Mapping for Sustainable Reservoir Management

Machine Learning-Driven Subsurface Zonation and Connectivity Mapping for Sustainable Reservoir Management | 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 Machine Learning-Driven Subsurface Zonation and Connectivity Mapping for Sustainable Reservoir Management Fossong Guilianno, Kingsley Onyekwere Okengwu, Ugochi Adaku Okengwu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9095534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study integrates advanced data analytics and machine learning techniques to optimize reservoir characterization and production strategies in the Gabo Field, Niger Delta. Utilizing k-means clustering, decline curve analysis (DCA), decision trees, and Deepseek-R1 large language model, we conducted comprehensive reservoir connectivity and AI zonation analyses, well placement optimization, and enhanced oil recovery (EOR) potential assessment. The methodology combines petrophysical data, production histories, and fluid contact depths to delineate reservoir zones, identify production regimes, and evaluate EOR candidates. Results reveal three distinct reservoir zones and significant heterogeneity in reservoir quality, with Zone C exhibiting superior porosity and permeability. Connectivity analysis identifies three compartments, highlighting the isolated nature of high-performing wells. The integration of machine learning models achieves 87% accuracy in EOR candidate identification and provides insights into optimal intervention strategies. This research demonstrates the potential of AI-driven approaches in reservoir management, offering a 40% reduction in analysis time and identifying bypassed zones with 40% higher recovery potential compared to traditional methods. The findings have implications for field development planning, production optimization, and the broader application of data analytics in petroleum systems. Connectivity Enhanced oil recovery (EOR) Machine learning Reservoir characterization Reservoir Data analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-9095534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615116197,"identity":"065cd66b-de13-49ed-bf2b-3bd89beb14f8","order_by":0,"name":"Fossong Guilianno","email":"data:image/png;base64,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","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":true,"prefix":"","firstName":"Fossong","middleName":"","lastName":"Guilianno","suffix":""},{"id":615116198,"identity":"c62d8061-69fd-4d21-b28a-094eb7db106c","order_by":1,"name":"Kingsley Onyekwere Okengwu","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"Kingsley","middleName":"Onyekwere","lastName":"Okengwu","suffix":""},{"id":615116199,"identity":"fb1ceb8a-3e2e-4e09-baa8-70207495e0e4","order_by":2,"name":"Ugochi Adaku Okengwu","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"Ugochi","middleName":"Adaku","lastName":"Okengwu","suffix":""}],"badges":[],"createdAt":"2026-03-11 14:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9095534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9095534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108098822,"identity":"7a2459c1-4b7c-4bbd-bf24-431b4f2a358e","added_by":"auto","created_at":"2026-04-29 10:26:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1092058,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptFossongMarch2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9095534/v1_covered_6c2c6894-9656-423a-b27f-47566a46e179.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-Driven Subsurface Zonation and Connectivity Mapping for Sustainable Reservoir Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Connectivity, Enhanced oil recovery (EOR), Machine learning, Reservoir characterization, Reservoir Data analytics","lastPublishedDoi":"10.21203/rs.3.rs-9095534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9095534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study integrates advanced data analytics and machine learning techniques to optimize reservoir characterization and production strategies in the Gabo Field, Niger Delta. Utilizing k-means clustering, decline curve analysis (DCA), decision trees, and Deepseek-R1 large language model, we conducted comprehensive reservoir connectivity and AI zonation analyses, well placement optimization, and enhanced oil recovery (EOR) potential assessment. The methodology combines petrophysical data, production histories, and fluid contact depths to delineate reservoir zones, identify production regimes, and evaluate EOR candidates. Results reveal three distinct reservoir zones and significant heterogeneity in reservoir quality, with Zone C exhibiting superior porosity and permeability. Connectivity analysis identifies three compartments, highlighting the isolated nature of high-performing wells. The integration of machine learning models achieves 87% accuracy in EOR candidate identification and provides insights into optimal intervention strategies. 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