Intelligent Hydraulic Flow Unit Mapping: Leveraging Unsupervised and Supervised Learning on Large-Scale Core Data

Intelligent Hydraulic Flow Unit Mapping: Leveraging Unsupervised and Supervised Learning on Large-Scale Core Data | 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 Intelligent Hydraulic Flow Unit Mapping: Leveraging Unsupervised and Supervised Learning on Large-Scale Core Data Mohammed Joobayear Hossain, Minhaz Chowdhury, Amad Hussen, Md Shofiqul Islam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370518/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 Accurate identification of hydraulic flow units (HFUs) is fundamental for reservoir characterization; however, conventional approaches, such as histogram analysis, log-log plots of reservoir quality index (RQI) versus porosity index (ϕz), and Z-score probability tests, often suffer from subjectivity, data overlap, and limited scalability across large datasets. To address these limitations, this study introduces a hybrid machine learning workflow that integrates unsupervised clustering and supervised classification models to automate the identification and prediction of HFUs. In the first phase, unsupervised models including K-means, K-medoids, Fuzzy C-means (FCM), and Gaussian mixture models (GMM) were employed to detect the optimal number of HFUs. The GMM demonstrated superior clustering performance (R² = 0.9278, RMSE = 0.3365) compared to K-means and K-medoids, whereas FCM underperformed. In the second phase, supervised learning models were applied to predict HFUs using laboratory-derived core features. Among the tested models, k-nearest neighbors (KNN), random forest classifier (RFC), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and a stacking hybrid were evaluated. RFC outperformed the others with robust generalization (training accuracy: 0.983; testing accuracy: 0.972), while SVM showed moderate success, and KNN exhibited overfitting. Boosting models, such as XGBoost and GB, achieved high training accuracy but suffered from overfitting. In contrast, AdaBoost demonstrated relatively lower performance but stronger generalization capabilities. The stacking model, though highly accurate in training, also displayed overfitting during testing. Computational efficiency analysis further highlighted the trade-off between training time and predictive performance, with KNN and SVM being the fastest but also the least reliable. At the same time, RFC provided the most balanced accuracy–time outcome. Overall, the proposed workflow establishes an effective and scalable methodology for HFU classification, offering greater consistency, objectivity, and applicability to large reservoir datasets in the Norwegian sector of the North Sea. Petroleum Engineering Petroleum Engineering Petroleum Engineering Hydraulic flow unit Reservoir characterization Machine learning Core data Boosting algorithm Full Text Additional Declarations The authors declare no competing interests. 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. 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-9370518","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620445352,"identity":"2dc3f213-e622-48e6-9816-97b211d46480","order_by":0,"name":"Mohammed Joobayear Hossain","email":"","orcid":"","institution":"Shahjalal University of Science And Technology","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Joobayear","lastName":"Hossain","suffix":""},{"id":620465206,"identity":"53899a21-f02d-4f74-86cf-a17ce1c3158c","order_by":1,"name":"Minhaz Chowdhury","email":"","orcid":"","institution":"Shahjalal University of Science And Technology","correspondingAuthor":false,"prefix":"","firstName":"Minhaz","middleName":"","lastName":"Chowdhury","suffix":""},{"id":620465207,"identity":"01aff865-7293-400d-ab85-c6b8fa1f4fdc","order_by":2,"name":"Amad Hussen","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Amad","middleName":"","lastName":"Hussen","suffix":""},{"id":620465208,"identity":"0fbc457e-d6c9-445d-827b-3208b54a133d","order_by":3,"name":"Md Shofiqul Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCQaGDwlgFvMBqFACQS2MMyBq2ECUAZFaICweA+K0yM9uPtjwoIwhT7e95/Nnnj9/GPjZcwzwajG4cyyxIeEcQ7HZmbPbpHnbDBgke94Q0CKRY/4gse1/4rYbuduYeRsMGAxuELBFfkaOYUNiG0PitvtvHgMdZsBgT0gLww2Ylhs8DNI8bAYgewk47EYazC9pZpJz24x5JM48KyDgsOSDjT+AIWZ2/PDjD2/+yMnxtydvwO8wMGBDxAUPEcrRtIyCUTAKRsEowAAAmjxI9p0AK00AAAAASUVORK5CYII=","orcid":"","institution":"Shahjalal University of Science And Technology","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Shofiqul","lastName":"Islam","suffix":""}],"badges":[],"createdAt":"2026-04-09 15:36:44","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9370518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9370518/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106590083,"identity":"9c787c48-d319-48e9-8d50-d57b6f214cab","added_by":"auto","created_at":"2026-04-10 08:26:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2840709,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9370518/v1_covered_b80c2d68-241d-4a29-b97e-822c5efc717f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntelligent Hydraulic Flow Unit Mapping: Leveraging Unsupervised and Supervised Learning on Large-Scale Core Data\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Shahjalal University of Science and Technology","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":"Hydraulic flow unit, Reservoir characterization, Machine learning, Core data, Boosting algorithm","lastPublishedDoi":"10.21203/rs.3.rs-9370518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9370518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate identification of hydraulic flow units (HFUs) is fundamental for reservoir characterization; however, conventional approaches, such as histogram analysis, log-log plots of reservoir quality index (RQI) versus porosity index (ϕz), and Z-score probability tests, often suffer from subjectivity, data overlap, and limited scalability across large datasets. 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RFC outperformed the others with robust generalization (training accuracy: 0.983; testing accuracy: 0.972), while SVM showed moderate success, and KNN exhibited overfitting. Boosting models, such as XGBoost and GB, achieved high training accuracy but suffered from overfitting. In contrast, AdaBoost demonstrated relatively lower performance but stronger generalization capabilities. The stacking model, though highly accurate in training, also displayed overfitting during testing. Computational efficiency analysis further highlighted the trade-off between training time and predictive performance, with KNN and SVM being the fastest but also the least reliable. At the same time, RFC provided the most balanced accuracy\u0026ndash;time outcome. 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