A deep learning-based surrogate and uncertainty quantification for fast electromagnetic modeling in complex formations

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyA deep learning-based surrogate and uncertainty quantification for fast electromagnetic modeling in complex formationsAuthors: Yuchen JinChaoxian QiLi YanYueqin HuangXuqing WuJiefu ChenYuchen JinUniversity of HoustonSearch for more papers by this author, Chaoxian QiUniversity of HoustonSearch for more papers by this author, Li YanUniversity of HoustonSearch for more papers by this author, Yueqin HuangCyentech Consulting LLCSearch for more papers by this author, Xuqing WuUniversity of HoustonSearch for more papers by this author, and Jiefu ChenUniversity of HoustonSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751935.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe employ the deep learning approach to build a high-fidelity surrogate for fast modeling of electromagnetic well logging measurements in high-dimensional complex formations. Due to the limitation of the computational resource, real-time applications are primarily dependent on 1D electromagnetic well logging forward solvers. The results are not accurate enough for revealing complicated underground geological structures. To improve the efficiency of the high-dimensional forward solvers, we propose a general framework for training independent surrogates in different geological cases. The uncertainty of the forward simulation is evaluated by the Monte-Carlo method. The efficiency and accuracy of the surrogate are validated using synthetic data of ultra-deep directional electromagnetic logging measurements. Compared to rigorous numerical methods such as the finite difference and the finite element methods, the computational cost of the proposed surrogate is significantly reduced. Numerical experiments show that the proposed framework enables real-time modeling and reconstruction of subsurface formations. The uncertainty quantification using the deep learning based surrogates is similar to that based on rigorous methods.Keywords: electromagnetic, modeling, surrogate, uncertainty quantification, deep learning, well-loggingPermalink: https://doi.org/10.1190/image2022-3751935.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Yuchen Jin, Chaoxian Qi, Li Yan, Yueqin Huang, Xuqing Wu, and Jiefu Chen, (2022), "A deep learning-based surrogate and uncertainty quantification for fast electromagnetic modeling in complex formations," SEG Technical Program Expanded Abstracts : 717-721. https://doi.org/10.1190/image2022-3751935.1 Plain-Language Summary Keywordselectromagnetic modelingsurrogateuncertainty quantificationdeep learningwell-loggingPDF DownloadLoading ...