Geophysical Well Log Multifractal Analysis and PLS-DA Modeling for Enhanced Oil–Gas Discrimination

Geophysical Well Log Multifractal Analysis and PLS-DA Modeling for Enhanced Oil–Gas Discrimination | 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 Geophysical Well Log Multifractal Analysis and PLS-DA Modeling for Enhanced Oil–Gas Discrimination Abdelbasset BOULASSEL, Soraya Makhlouf, Fethi Ali Cheddad, Zinelabidine BOUMELIT, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7338533/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Acta Geophysica → Version 1 posted 6 You are reading this latest preprint version Abstract Accurate identification of reservoir fluids is essential for effective exploration and production in the petroleum industry. Traditional petrophysical methods often struggle to distinguish between oil and gas, particularly in geologically complex formations. This study applies advanced fractal and multifractal analyses to geophysical well log data to improve fluid discrimination. Fractal measurements—such as box dimension, regularization dimension, and multifractal spectrum attributes (including spectrum width, peak singularity strength, minimum and maximum singularity strengths, corresponding spectrum values, and the C value)—were evaluated for their ability to differentiate fluids based on distinct log signatures. Partial Least Squares Discriminant Analysis (PLS-DA), a multivariate statistical modeling technique, was employed to uncover patterns between these features and fluid types. A dataset of 1085 geophysical well logs, including gamma ray, acoustic, density, neutron porosity, resistivity, photoelectric factor, and gamma ray spectroscopy measurements, was used to extract attributes. The PLS-DA model achieved strong performance, with Q² cumulative predictive ability values above 0.8, R²Y cumulative explained variance in the response matrix values exceeding 0.9, R²X cumulative explained variance in the predictor matrix values above 0.85, and AUC (area under the receiver operating characteristic curve) scores over 0.95 in both training and validation. A key outcome is a Classification Function that combines multiple attributes into a single score, enhancing prediction accuracy and interpretability. The methodology also quantifies the relative importance of each attribute, offering insight into the petrophysical processes reflected in well logs. This integrated approach provides a robust, interpretable, and data-driven solution for improved fluid identification in petroleum reservoirs. Reservoir Characterization Multifractal Analysis Fluid Discrimination Geophysical Well Logs PLS-DA Oil and Gas Full Text Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Acta Geophysica → Version 1 posted Editorial decision: Major revisions 07 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 23 Aug, 2025 First submitted to journal 16 Aug, 2025 Editor assigned by journal 13 Aug, 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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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-7338533","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512051900,"identity":"086c04ef-76c0-42cd-a386-ad0558abac31","order_by":0,"name":"Abdelbasset 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Traditional petrophysical methods often struggle to distinguish between oil and gas, particularly in geologically complex formations. This study applies advanced fractal and multifractal analyses to geophysical well log data to improve fluid discrimination. Fractal measurements\u0026mdash;such as box dimension, regularization dimension, and multifractal spectrum attributes (including spectrum width, peak singularity strength, minimum and maximum singularity strengths, corresponding spectrum values, and the C value)\u0026mdash;were evaluated for their ability to differentiate fluids based on distinct log signatures. Partial Least Squares Discriminant Analysis (PLS-DA), a multivariate statistical modeling technique, was employed to uncover patterns between these features and fluid types. 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