Interpretable Ensemble Meta-Learning for Supervised Prediction of Estimated Ultimate Recovery in Shale Gas Wells

Interpretable Ensemble Meta-Learning for Supervised Prediction of Estimated Ultimate Recovery in Shale Gas Wells | 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 Interpretable Ensemble Meta-Learning for Supervised Prediction of Estimated Ultimate Recovery in Shale Gas Wells Runshi Huo, Benjieming Liu, Han Dong, Pengfei Li, Yuping Sun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8955225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Accurately assessing the Estimated Ultimate Recovery (EUR) of shale gas wells is crucial for informed decision-making in development planning and maximizing economic benefits. However, the complex geological conditions and engineering schemes involved create a unique and intricate flow environment, making EUR highly sensitive to the interplay of geological, engineering, and production factors. Given the complexity of flow mechanisms and the lack of a clear, standardized calculation method, reliably predicting EUR remains a challenge. While deep learning models offer substantial predictive power, their "black-box" nature limits trust among decision-makers, and current models often neglect interpretability. To address these challenges, we introduce the LMTR framework, a meta-learner ensemble model tailored for EUR prediction in shale gas wells. LMTR integrates Lasso and Ridge regularization at its two ends, effectively mitigating multicollinearity and ensuring a stable linear structure. This scaffold supports a multi-layered architecture, where a Multi-Layer Perceptron (MLP) captures nonlinear relationships, and a Transformer component models long-range feature interactions. This hybrid design strikes a balance between interpretability and predictive capability, offering a robust framework for EUR estimation.The model's performance was evaluated using Mean Squared Error (MSE) and R² metrics, with cross-validation conducted via a leave-one-out method. Comparative experiments with 11 baseline models demonstrate superior predictive accuracy of LMTR, suggesting its potential as a decision-support tool for early-stage shale gas development. Additionally, the framework enhances interpretability through SHAP analysis and integrates Net Present Value (NPV) calculations, guiding engineering optimization based on geological variability. The results indicate that LMTR outperforms existing approaches in EUR prediction and provides a reliable basis for optimizing development strategies. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences Shale gas EUR Explainable AI(XAI) Meta-learner Ensemble learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Editor invited by journal 11 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 04 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. 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. 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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-8955225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606832802,"identity":"b841b54c-b8ec-442f-a878-5db3354bc172","order_by":0,"name":"Runshi 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However, the complex geological conditions and engineering schemes involved create a unique and intricate flow environment, making EUR highly sensitive to the interplay of geological, engineering, and production factors. Given the complexity of flow mechanisms and the lack of a clear, standardized calculation method, reliably predicting EUR remains a challenge. While deep learning models offer substantial predictive power, their \"black-box\" nature limits trust among decision-makers, and current models often neglect interpretability. To address these challenges, we introduce the LMTR framework, a meta-learner ensemble model tailored for EUR prediction in shale gas wells. LMTR integrates Lasso and Ridge regularization at its two ends, effectively mitigating multicollinearity and ensuring a stable linear structure. This scaffold supports a multi-layered architecture, where a Multi-Layer Perceptron (MLP) captures nonlinear relationships, and a Transformer component models long-range feature interactions. This hybrid design strikes a balance between interpretability and predictive capability, offering a robust framework for EUR estimation.The model's performance was evaluated using Mean Squared Error (MSE) and R\u0026sup2; metrics, with cross-validation conducted via a leave-one-out method. Comparative experiments with 11 baseline models demonstrate superior predictive accuracy of LMTR, suggesting its potential as a decision-support tool for early-stage shale gas development. Additionally, the framework enhances interpretability through SHAP analysis and integrates Net Present Value (NPV) calculations, guiding engineering optimization based on geological variability. 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