Conditional Generative Adversarial Networks for Subsurface Scenario-Based Uncertainty

Conditional Generative Adversarial Networks for Subsurface Scenario-Based Uncertainty | 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 Conditional Generative Adversarial Networks for Subsurface Scenario-Based Uncertainty Ahmed Merzoug, Michael Pyrcz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7801111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Subsurface modeling workflows are critical for multi-million-dollar decisions in various subsurface resources, yet they are significantly impacted by inherent uncertainties. While realization-based uncertainty is commonly addressed, scenario-based uncertainty, which accounts for different conceptual geological models and their parameters, is often overlooked due to its time-consuming and iterative nature. Recently, generative artificial intelligence (GenAI) models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, have shown promise in subsurface modeling and history matching, primarily for realization-based uncertainty quantification. However, their application to scenario-based uncertainty remains limited, often relying on modifications to loss functions that introduce training instabilities. We propose a novel workflow for quantifying scenario-based uncertainty in subsurface modeling using a conditional Wasserstein generative adversarial network with a gradient penalty (WGAN-GP), integrated with conditional batch normalization and a projection discriminator. Our approach implicitly learns label conditioning, eliminating the need for explicit conditioning loss terms and promoting more stable training, and simpler parameter tuning. The proposed model supports multiple continuous conditioning labels, including object orientation, proportion, and width, and employs a trigonometric approach for parameterizing orientation. We demonstrate our workflow on a 2D synthetic channel reservoir, incorporating it into a data assimilation framework using Ensemble Smoother Multiple Data Assimilation (ES-MDA) to condition well locations and derive conditioning labels. Quantitative and qualitative checks confirm the model's ability to accurately reproduce and interpolate various geological model parameters, while maintaining geological consistency and realization-based uncertainty. subsurface modeling conditional generative adversarial networks scenario-based uncertainty Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-7801111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526071475,"identity":"dcae1adb-d5e4-426d-81e6-4829d49933b6","order_by":0,"name":"Ahmed 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Uncertainty\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas at Austin","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"subsurface modeling, conditional generative adversarial networks, scenario-based uncertainty","lastPublishedDoi":"10.21203/rs.3.rs-7801111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7801111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSubsurface modeling workflows are critical for multi-million-dollar decisions in various subsurface resources, yet they are significantly impacted by inherent uncertainties. While realization-based uncertainty is commonly addressed, scenario-based uncertainty, which accounts for different conceptual geological models and their parameters, is often overlooked due to its time-consuming and iterative nature. Recently, generative artificial intelligence (GenAI) models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models, have shown promise in subsurface modeling and history matching, primarily for realization-based uncertainty quantification. However, their application to scenario-based uncertainty remains limited, often relying on modifications to loss functions that introduce training instabilities.\u003c/p\u003e\u003cp\u003eWe propose a novel workflow for quantifying scenario-based uncertainty in subsurface modeling using a conditional Wasserstein generative adversarial network with a gradient penalty (WGAN-GP), integrated with conditional batch normalization and a projection discriminator. Our approach implicitly learns label conditioning, eliminating the need for explicit conditioning loss terms and promoting more stable training, and simpler parameter tuning. The proposed model supports multiple continuous conditioning labels, including object orientation, proportion, and width, and employs a trigonometric approach for parameterizing orientation. We demonstrate our workflow on a 2D synthetic channel reservoir, incorporating it into a data assimilation framework using Ensemble Smoother Multiple Data Assimilation (ES-MDA) to condition well locations and derive conditioning labels. Quantitative and qualitative checks confirm the model's ability to accurately reproduce and interpolate various geological model parameters, while maintaining geological consistency and realization-based uncertainty.\u003c/p\u003e","manuscriptTitle":"Conditional Generative Adversarial Networks for Subsurface Scenario-Based Uncertainty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-09 05:19:18","doi":"10.21203/rs.3.rs-7801111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be784709-9c87-43f1-a421-1e2434d50e45","owner":[],"postedDate":"October 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T05:19:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-09 05:19:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7801111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7801111","identity":"rs-7801111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}