A Multi-Physics Coupling Driven Method for Initial Wellbore Trajectory Design

A Multi-Physics Coupling Driven Method for Initial Wellbore Trajectory Design | 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 A Multi-Physics Coupling Driven Method for Initial Wellbore Trajectory Design Guo Shuai, Zhikun Liu, Qi Li, Liupeng Wang, Fengtao Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8071937/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Wellbore trajectory design in complex geological formations is constrained by formation heterogeneity, structural discontinuities, and evolving in-situ stresses, making traditional geometry- or empiricism-based methods inadequate. This study introduces an innovative multi-physics coupling-driven framework that unifies geological modeling, drillstring statics, pore pressure–stress fields, and probabilistic risk mapping into a quantitatively constrained optimization system. The key innovation lies in coupling geo–mechanical stability analysis with Bayesian probability updating, enabling dynamic assessment of formation uncertainty and real-time trajectory feasibility. A B-spline-based parameterization combined with a hybrid intelligent optimizer ensures global convergence while balancing curvature smoothness and wellbore stability margin. Case results from a shale–sandstone formation in the Sichuan–Chongqing region demonstrate that the proposed approach reduces trajectory length by 7.1%, maximum dogleg severity by 35.4%, and high-risk interval penetration by 64%, with an 18.1% increase in reservoir exposure. This framework establishes a new paradigm for intelligent, uncertainty-aware trajectory design, effectively bridging formation physics, mechanical response, and probabilistic risk prediction. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Solid earth sciences Multi-physics coupling Wellbore trajectory optimization Geo-mechanical modeling Probabilistic risk mapping Bayesian uncertainty integration Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Dec, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor invited by journal 14 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 09 Nov, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8071937","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":550638519,"identity":"a7dc6718-7e31-45d3-9423-484b5cfbf1cb","order_by":0,"name":"Guo Shuai","email":"","orcid":"","institution":"Xi’an Shiyou University","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Shuai","suffix":""},{"id":550638520,"identity":"402cc71d-cd30-43b5-b6a1-f8fd5e6d40ce","order_by":1,"name":"Zhikun 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trajectory optimization, Geo-mechanical modeling, Probabilistic risk mapping, Bayesian uncertainty integration","lastPublishedDoi":"10.21203/rs.3.rs-8071937/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8071937/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWellbore trajectory design in complex geological formations is constrained by formation heterogeneity, structural discontinuities, and evolving in-situ stresses, making traditional geometry- or empiricism-based methods inadequate. This study introduces an innovative multi-physics coupling-driven framework that unifies geological modeling, drillstring statics, pore pressure\u0026ndash;stress fields, and probabilistic risk mapping into a quantitatively constrained optimization system. The key innovation lies in coupling geo\u0026ndash;mechanical stability analysis with Bayesian probability updating, enabling dynamic assessment of formation uncertainty and real-time trajectory feasibility. A B-spline-based parameterization combined with a hybrid intelligent optimizer ensures global convergence while balancing curvature smoothness and wellbore stability margin. Case results from a shale\u0026ndash;sandstone formation in the Sichuan\u0026ndash;Chongqing region demonstrate that the proposed approach reduces trajectory length by 7.1%, maximum dogleg severity by 35.4%, and high-risk interval penetration by 64%, with an 18.1% increase in reservoir exposure. 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