Fluid Property Identification of CatBoost Model Based on Grey Wolf Optimization Algorithm

Fluid Property Identification of CatBoost Model Based on Grey Wolf Optimization Algorithm | 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 Fluid Property Identification of CatBoost Model Based on Grey Wolf Optimization Algorithm Shuzhen Zhao, Zhao Bin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7526564/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Low porosity and permeability reservoirs play an important role in the development of oil and gas resources. This kind of reservoir has strong heterogeneity, complex pore structure, and unclear characteristics of log responses. Conventional methods, therefore, are difficult to identify its fluid properties. This study explores the adoption of GWO-CatBoost model for fluid properties’ identification. Firstly, the GWO algorithm is used to globally optimize the core parameters of the CatBoost model, thereby enhancing the generalization performance and predictive accuracy of the model. Secondly, the logging curves of the study area were input into the optimized model to achieve high-precision fluid property identification. To test the validity of this method, this study selected CatBoost model, random forest (RF), logistic regression (LR), and LightGBM model for comparative experiments. The findings show that the GWO-CatBoost model has superior accuracy of fluid identification, reaching 92.78%, which is significantly improved compared with other models. This provides a new idea for fluid identification in low porosity and permeability reservoirs. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences Complicated reservoirs CatBoost Fluid identification Grey wolf optimization algorithm GWO-CatBoost Parameter intelligent optimization Well-logging Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers invited by journal 06 Dec, 2025 Editor invited by journal 21 Oct, 2025 Editor assigned by journal 08 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 03 Sep, 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-7526564","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558104429,"identity":"19d7072d-bf78-4f06-9ae7-1cb9e9b648ac","order_by":0,"name":"Shuzhen Zhao","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Shuzhen","middleName":"","lastName":"Zhao","suffix":""},{"id":558104437,"identity":"8d639144-0d0b-4724-beee-24422b986d39","order_by":1,"name":"Zhao 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