Development of a Data-Driven Framework for Wellhead Pressure Prediction in Hydraulic Fracture Operations

Development of a Data-Driven Framework for Wellhead Pressure Prediction in Hydraulic Fracture Operations | 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 Development of a Data-Driven Framework for Wellhead Pressure Prediction in Hydraulic Fracture Operations Mobina Yavari, Ehsan Moosavi, Reza Shirinabadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6700774/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Pressure prediction is essential for optimizing hydraulic fracturing (HF) performance and mitigating operational risks. By analyzing complex patterns and processing large volumes of data, data-driven methods exhibit better accuracy and greater potential than traditional methods, thereby ensuring optimal prediction during HF operations. The present study is an attempt to provide the possibility of predicting wellhead pressure (WHP) using data-driven models (DDMs), including random forest (RF), convolutional neural networks (CNN), and support vector machine (SVM). The effectiveness of the models was evaluated based on operational data derived from the McCully gas field. The prediction results demonstrated that the RF model had the highest accuracy with an R-squared correlation ( \(\:{\text{R}}^{2}\) ) of 0.9517 for the experimental dataset. Also, it has mean absolute error (MAE) and root mean square error (RMSE) values of 0.37 and 0.081, respectively, indicating the minimum error of the model in WHP prediction. In addition, analyzing the injection rate (IR) and pressure drop trend using the RF model could help properly diagnose the behavioral pattern of sudden pressure changes. These results proved the reliability and effectiveness of the RF model for WHP prediction, which can contribute considerably to HF design optimization, operational risk mitigation, and reservoir performance maximization in the future. Hydraulic fracturing data-driven methods injection rate wellhead pressure prediction optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviews received at journal 06 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 23 Aug, 2025 Editor assigned by journal 31 May, 2025 Submission checks completed at journal 28 May, 2025 First submitted to journal 19 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. We do this by developing innovative software and high quality services for the global research community. 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