Analysis and Prediction of Hydrogen Relative Permeability in Underground Storage Systems Using Machine Learning

Analysis and Prediction of Hydrogen Relative Permeability in Underground Storage Systems Using Machine Learning | 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 Analysis and Prediction of Hydrogen Relative Permeability in Underground Storage Systems Using Machine Learning Alireza Amjadi, Shahin Kord This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6735554/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Underground hydrogen storage (UHS) is emerging as a critical component of future hydrogen infrastructure, offering a reliable solution for energy storage and supply security. A key parameter influencing the efficiency of UHS is the relative permeability of hydrogen (H₂), which governs the flow dynamics of hydrogen in subsurface formations. Accurate prediction of H₂ relative permeability is essential for optimizing storage systems, yet traditional empirical models often fail to capture the complex interactions in hydrogen-water systems. In this study, advanced machine learning (ML) techniques, including Polynomial Regression, Multi-Layer Perceptron (MLP), Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Random Forest Regression (RFR), and Gradient Boosting Regression (GBR), were employed to predict H₂ relative permeability under various experimental conditions. The dataset, comprising 130 data points, included variables such as gas saturation, porosity, salinity, and differential pressure. Among the models tested, Gaussian Process Regression (GPR) demonstrated superior performance, achieving an R² of 0.9356, a Root Mean Squared Error (RMSE) of 0.0280, and a Mean Absolute Error (MAE) of 0.0178. These results highlight the potential of machine learning to provide accurate and efficient predictions, significantly outperforming traditional empirical approaches. The findings underscore the importance of integrating machine learning into UHS research, offering a robust framework for predicting hydrogen flow behavior in porous media. Future research should focus on expanding the dataset and incorporating additional factors such as hysteresis and gas mixing effects to further enhance the predictive accuracy of these models. This study contributes to the advancement of sustainable energy solutions by improving the design and operation of underground hydrogen storage systems. Physical sciences/Energy science and technology Physical sciences/Engineering Hydrogen Storage Machine Learning Relative Permeability Gaussian Process Regression Underground Storage Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers invited by journal 09 Jun, 2025 Editor assigned by journal 02 Jun, 2025 Editor invited by journal 28 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 23 May, 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. 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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-6735554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468950481,"identity":"4c13f732-7ed7-4f4d-8b18-cab6f7cd421a","order_by":0,"name":"Alireza Amjadi","email":"data:image/png;base64,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","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Amjadi","suffix":""},{"id":468950482,"identity":"c212c9eb-ad9a-46fe-8c9c-fe68d204ddaa","order_by":1,"name":"Shahin Kord","email":"","orcid":"","institution":"Petroleum University of Technology (PUT)","correspondingAuthor":false,"prefix":"","firstName":"Shahin","middleName":"","lastName":"Kord","suffix":""}],"badges":[],"createdAt":"2025-05-23 20:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6735554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6735554/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21507-3","type":"published","date":"2025-10-28T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95040504,"identity":"5e79265d-da1a-49bc-b963-580170706405","added_by":"auto","created_at":"2025-11-03 16:09:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981734,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsFormattedReady.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6735554/v1_covered_c7dba0c4-439f-44e3-93c5-8a8709d59d0a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis and Prediction of Hydrogen Relative Permeability in Underground Storage Systems Using Machine Learning","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":"Hydrogen Storage, Machine Learning, Relative Permeability, Gaussian Process Regression, Underground Storage Systems","lastPublishedDoi":"10.21203/rs.3.rs-6735554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6735554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderground hydrogen storage (UHS) is emerging as a critical component of future hydrogen infrastructure, offering a reliable solution for energy storage and supply security. A key parameter influencing the efficiency of UHS is the relative permeability of hydrogen (H₂), which governs the flow dynamics of hydrogen in subsurface formations. Accurate prediction of H₂ relative permeability is essential for optimizing storage systems, yet traditional empirical models often fail to capture the complex interactions in hydrogen-water systems.\u003c/p\u003e\n\u003cp\u003eIn this study, advanced machine learning (ML) techniques, including Polynomial Regression, Multi-Layer Perceptron (MLP), Gaussian Process Regression (GPR), Kernel Ridge Regression (KRR), Random Forest Regression (RFR), and Gradient Boosting Regression (GBR), were employed to predict H₂ relative permeability under various experimental conditions. The dataset, comprising 130 data points, included variables such as gas saturation, porosity, salinity, and differential pressure. 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