AI-Driven Production Forecasting: Integrating LSTM, Prophet, and Random Forest

AI-Driven Production Forecasting: Integrating LSTM, Prophet, and Random Forest | 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 AI-Driven Production Forecasting: Integrating LSTM, Prophet, and Random Forest Guilianno Fossong, Kingsley Okengwu, Ugochi Okengwu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9095017/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract This study improves production forecasting for a heterogeneous Niger Delta reservoir using an ensemble of Long Short-Term Memory (LSTM), Prophet, and Random Forest (RF) models. Thirty-two years (1992–2024) of Gabo Field, production data were analyzed, and a five-year forecast was generated through a workflow combining Decline Curve Analysis with advanced machine learning. Ensemble stacking with XGBoost delivered the most reliable performance (MASE < 1), demonstrating consistent improvement over standalone models. Random Forest showed high predictive strength (R² = 0.98–0.99), while LSTM and Prophet captured temporal and seasonal patterns that enhanced ensemble robustness. Integration with Deepseek-R1 cognitive analysis aided identification of reservoir heterogeneities and supported improved decision-making. Results highlight the value of hybrid physics-informed and AI-driven workflows for production forecasting and reservoir management. The study demonstrates how combining traditional reservoir engineering with machine learning and cognitive tools can enhance forecast reliability and optimize development planning in mature oil fields. Ensemble machine learning Production forecasting Reservoir modeling Niger Delta Computational geoscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 27 Mar, 2026 Editor invited by journal 23 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 11 Mar, 2026 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. 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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-9095017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618037325,"identity":"5e3cbe99-1094-4fbc-8f4b-57b11cd18847","order_by":0,"name":"Guilianno Fossong","email":"data:image/png;base64,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","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":true,"prefix":"","firstName":"Guilianno","middleName":"","lastName":"Fossong","suffix":""},{"id":618037328,"identity":"dd6be86b-adaa-4206-9d1a-bf6c1f71c82f","order_by":1,"name":"Kingsley Okengwu","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"Kingsley","middleName":"","lastName":"Okengwu","suffix":""},{"id":618037329,"identity":"e6c16332-6d50-4be3-9f29-b1cee3423f31","order_by":2,"name":"Ugochi Okengwu","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"Ugochi","middleName":"","lastName":"Okengwu","suffix":""}],"badges":[],"createdAt":"2026-03-11 13:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9095017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9095017/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106479209,"identity":"a4a49fde-c839-4c7a-bf8d-9dc6702db33a","added_by":"auto","created_at":"2026-04-09 04:10:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2426793,"visible":true,"origin":"","legend":"","description":"","filename":"FOSSONGSUBMISSIONMARCH2026FINAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9095017/v1_covered_1aeee600-76db-4b97-982b-e250afc639dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Driven Production Forecasting: Integrating LSTM, Prophet, and Random Forest","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Ensemble machine learning, Production forecasting, Reservoir modeling, Niger Delta, Computational geoscience","lastPublishedDoi":"10.21203/rs.3.rs-9095017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9095017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study improves production forecasting for a heterogeneous Niger Delta reservoir using an ensemble of Long Short-Term Memory (LSTM), Prophet, and Random Forest (RF) models. 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