Prediction of Deep Formation Pressure Using CNN-LSTM Method

Prediction of Deep Formation Pressure Using CNN-LSTM Method | 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 Prediction of Deep Formation Pressure Using CNN-LSTM Method Leng Huang, Yizhou Lu, Jiacheng Huang, Shiqi Zhang, Kanyu Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6586735/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 prediction of reservoir pressure, particularly in deep formations, is crucial for ensuring drilling safety and optimizing hydrocarbon exploration efficiency. Traditional empirical or semi-empirical methods often suffer from limited adaptability and weak generalization capability due to their reliance on simplified assumptions and predefined parameters. To address these limitations, this study proposes a deep learning framework based on a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture for reservoir pressure prediction. The model leverages the feature extraction capability of CNNand the temporal sequence learning strength of LSTM to effectively capture nonlinear patterns in well log data. A dataset comprising logging and measured pressure data from ten wells in Block A of a Chinese basin was used for training and validation. Principal Component Analysis (PCA) was employed for feature selection and dimensionality reduction. The performance of the proposed CNN-LSTM model was benchmarked against BP, CNN-BP, and CNN-RNN models using standard evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results indicate that the CNN-LSTM model achieved superior predictive accuracy, demonstrating its effectiveness in handling complex geological conditions and offering a robust approach for overpressure identification in deep reservoirs. Earth and environmental sciences/Solid earth sciences Physical sciences/Engineering 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. 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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-6586735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466412619,"identity":"92d1b552-72b2-409d-b317-bc55e6628c90","order_by":0,"name":"Leng Huang","email":"","orcid":"","institution":"Wuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Leng","middleName":"","lastName":"Huang","suffix":""},{"id":466412620,"identity":"786fdc5c-383c-46b0-a81b-d8ecadfb6599","order_by":1,"name":"Yizhou Lu","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Yizhou","middleName":"","lastName":"Lu","suffix":""},{"id":466412621,"identity":"2ccc4271-8aec-4998-89ac-e16b0b5af2a4","order_by":2,"name":"Jiacheng Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACxgYGZgYGG4YENiD7wQegCBs7UVrSwFqYDWeAtDATtgiiBaRYmgfKx6++/fBjgw8Jdnl80s3PpG1+bZPnY2Zg/PAxB4/DetKME2ckJBezyRwzts7tu23YxszALDlzGz6/5DAf5v1xILFNIsHwdm7PbUagFjZmXnxa+t8wH+ZJAGlJ/yBt2XPbnrCWGTnMyRAtOUbSDD9uJxKh5ZmxIdAvIC3Fhr0Nt5PbmBmb8frFsD/5sQQwxBLnz0jf+ODHn9u289ubD374iE9LA4qdbWCyAZtKOJBH5f7Bq3gUjIJRMApGKAAA3b5PXoEbYwEAAAAASUVORK5CYII=","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":true,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Huang","suffix":""},{"id":466412622,"identity":"9325e8d4-9c4e-4094-a347-0359de81f39d","order_by":3,"name":"Shiqi Zhang","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Zhang","suffix":""},{"id":466412623,"identity":"333c808a-143a-4ca1-a971-6f67ec511f11","order_by":4,"name":"Kanyu Su","email":"","orcid":"","institution":"Northeast Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Kanyu","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-05-04 05:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6586735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6586735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84531668,"identity":"57a604cd-fe23-46ce-8238-959c8eb05084","added_by":"auto","created_at":"2025-06-13 06:16:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":300905,"visible":true,"origin":"","legend":"","description":"","filename":"rpqddfvxddftzntmfwspgjmbyqkkjprr.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6586735/v1_covered_ae0ebcbe-e772-4d25-aa3f-df064acd585f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Deep Formation Pressure Using CNN-LSTM Method","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6586735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6586735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate prediction of reservoir pressure, particularly in deep formations, is crucial for ensuring drilling safety and optimizing hydrocarbon exploration efficiency. Traditional empirical or semi-empirical methods often suffer from limited adaptability and weak generalization capability due to their reliance on simplified assumptions and predefined parameters. To address these limitations, this study proposes a deep learning framework based on a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture for reservoir pressure prediction. The model leverages the feature extraction capability of CNNand the temporal sequence learning strength of LSTM to effectively capture nonlinear patterns in well log data. A dataset comprising logging and measured pressure data from ten wells in Block A of a Chinese basin was used for training and validation. Principal Component Analysis (PCA) was employed for feature selection and dimensionality reduction. 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