A Hybrid Computational Fluid Dynamics – Machine Learning Approach for Characterising CO 2 based Enhanced Oil Recovery in Naturally Fractured Reservoirs

A Hybrid Computational Fluid Dynamics – Machine Learning Approach for Characterising CO 2 based Enhanced Oil Recovery in Naturally Fractured Reservoirs | 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 Article A Hybrid Computational Fluid Dynamics – Machine Learning Approach for Characterising CO 2 based Enhanced Oil Recovery in Naturally Fractured Reservoirs Taimoor Asim, Khaliq ur-Rahman, Haval Kukha Hawez, Rakesh Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313230/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract CO 2 based Enhanced Oil Recovery (EOR) in unconventional reservoirs is an emerging technology. Scientific research efforts are directed towards understanding the propagation of CO 2 front due to the complex interplay between CO 2 injection and saturation, and reservoir’s constitutive relationships. Conventional methods for characterising CO 2 -EOR rely on high-fidelity numerical solutions that often result in over or under prediction of CO 2 geosequestration. In this study, we develop a novel hybrid Computational Fluid Dynamics (CFD) and Machine Learning (ML) framework that allows for rapid CO 2 geosequestration prediction and its optimal injection. Very low or high injection rates have been shown to result in low sweep efficiency or excessive entry pressure, while an intermediate injection rate offers the best balance between the two. CFD data-driven Gaussian Process Regression (GPR) and Extreme Gradient Boosting (XGBoost) models have been developed, trained and tested for predicting CO 2 saturation in the reservoir. Comparative analysis indicates that GPR outperforms XGBoost in terms of its predictive performance and robustness. Through the analysis of layer-resolved CO 2 front displacement and development of data-driven surrogate models, this study contributes a novel framework for CO 2 -EOR predictive modelling and optimising injection strategies in naturally fractured reservoirs. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing CO2 Geosequestration Enhanced Oil Recovery (EOR) Naturally Fractured Reservoirs Computational Fluid Dynamics (CFD) Machine Learning (ML) Discrete Fracture Model (DFM) Gaussian Process Regression (GPR) Extreme Gradient Boosting (XGBoost) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 07 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor invited by journal 13 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 06 Aug, 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. 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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-7313230","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501287137,"identity":"49918200-674a-4172-bdc1-0eab99e7b8e3","order_by":0,"name":"Taimoor Asim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLCCBAYbONuAoGoeiJY0UrUwMBwmQYs9A/PjDw9zzufLRyQwfvjBcNiYCFvYzCQSt9223HgjgVmyh+GwGTEOM2MAajEwnJHAIA10oQ0RWtg/f0jcdg6khfk3kVp4DIAOO2AgL5HABrKFCIcd5ikDakk2MOB52GbZY5BO2Pvs7e2bP/7cZmcg3558+MaPCmvDBoJ6mKG0wQHGBmJiBQnIEzZ9FIyCUTAKRioAABFCM4cLUyvbAAAAAElFTkSuQmCC","orcid":"","institution":"Robert Gordon University","correspondingAuthor":true,"prefix":"","firstName":"Taimoor","middleName":"","lastName":"Asim","suffix":""},{"id":501287140,"identity":"ba61605f-0048-4467-af98-bdc25f4bb7f1","order_by":1,"name":"Khaliq ur-Rahman","email":"","orcid":"","institution":"Robert Gordon University","correspondingAuthor":false,"prefix":"","firstName":"Khaliq","middleName":"","lastName":"ur-Rahman","suffix":""},{"id":501287142,"identity":"9c46798a-eab7-472c-af67-52cb2264855b","order_by":2,"name":"Haval Kukha Hawez","email":"","orcid":"","institution":"Koya University, Kurdistan Region – F.R","correspondingAuthor":false,"prefix":"","firstName":"Haval","middleName":"Kukha","lastName":"Hawez","suffix":""},{"id":501287145,"identity":"36abb0ac-82e4-4cbc-939f-aecc57458c6b","order_by":3,"name":"Rakesh Mishra","email":"","orcid":"","institution":"University of Huddersfield","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-08-06 22:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7313230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7313230/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34786-7","type":"published","date":"2026-01-07T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100070540,"identity":"a1ec6d8c-8e94-4269-85aa-0c2c06abae4e","added_by":"auto","created_at":"2026-01-12 16:18:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1903396,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313230/v1_covered_adc02fd0-60a4-4711-bca2-da54e6492ab7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid Computational Fluid Dynamics – Machine Learning Approach for Characterising CO 2 based Enhanced Oil Recovery in Naturally Fractured Reservoirs","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CO2 Geosequestration, Enhanced Oil Recovery (EOR), Naturally Fractured Reservoirs, Computational Fluid Dynamics (CFD), Machine Learning (ML), Discrete Fracture Model (DFM), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGBoost)","lastPublishedDoi":"10.21203/rs.3.rs-7313230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7313230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e based Enhanced Oil Recovery (EOR) in unconventional reservoirs is an emerging technology. Scientific research efforts are directed towards understanding the propagation of CO\u003csub\u003e2\u003c/sub\u003e front due to the complex interplay between CO\u003csub\u003e2\u003c/sub\u003e injection and saturation, and reservoir\u0026rsquo;s constitutive relationships. Conventional methods for characterising CO\u003csub\u003e2\u003c/sub\u003e-EOR rely on high-fidelity numerical solutions that often result in over or under prediction of CO\u003csub\u003e2\u003c/sub\u003e geosequestration. In this study, we develop a novel hybrid Computational Fluid Dynamics (CFD) and Machine Learning (ML) framework that allows for rapid CO\u003csub\u003e2\u003c/sub\u003e geosequestration prediction and its optimal injection. Very low or high injection rates have been shown to result in low sweep efficiency or excessive entry pressure, while an intermediate injection rate offers the best balance between the two. 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