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• W3198178302 abstract "PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsTarget-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior informationAuthors: Yuanyuan LiAndrey BakulinPhilippe NivletRobert SmithTariq AlkhalifahYuanyuan LiKing Abdullah University of Science and Technology (KAUST)Search for more papers by this author, Andrey BakulinEXPEC ARC, Saudi AramcoSearch for more papers by this author, Philippe NivletEXPEC ARC, Saudi AramcoSearch for more papers by this author, Robert SmithEXPEC ARC, Saudi AramcoSearch for more papers by this author, and Tariq AlkhalifahKing Abdullah University of Science and Technology (KAUST)Search for more papers by this authorhttps://doi.org/10.1190/segam2021-3581711.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractTime-lapse (TL) monitoring of the elastic property changes in the reservoir of interest is important for optimizing the reservoir interpretation and development plan. Given that elastic full-waveform inversion (EFWI) provides quantitative estimations of the elastic properties (Vp and Vs), its application to time-lapse elastic data is of considerable interest. For practical applications in reservoir monitoring, we need EFWI to provide high-resolution reservoir information at a reasonable cost. Thus, we develop an elastic redatuming technique to provide the required virtual elastic data for a target-oriented inversion, thus improving the computational efficiency by focusing our full-band inversion on the target zone. To improve the inversion resolution, we combine the well information and seismic data in the proposed time-lapse inversion approach using a regularized objective function. To derive the required prior model, we train a deep neural network (DNN) to learn the connection between the seismic estimation and the facies interpreted from well logs. We then apply the trained network to the target inversion domain to predict a prior model. Given the prior model, we perform another time-lapse inversion. We fit the simulated data difference for the virtual survey to the redatumed one from the surface recording and fit the model changes to the predicted prior model. The numerical results demonstrate that the proposed method enables the recovery of the time-lapse changes effectively in the target zone by incorporating the learned model changes from well logs.Keywords: elastic, time-lapse, full-waveform inversion, machine learning, datumingPermalink: https://doi.org/10.1190/segam2021-3581711.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Yuanyuan Li, Andrey Bakulin, Philippe Nivlet, Robert Smith, and Tariq Alkhalifah, (2021), Target-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior information, SEG Technical Program Expanded Abstracts : 3490-3494. https://doi.org/10.1190/segam2021-3581711.1 Plain-Language Summary Keywordselastictime-lapsefull-waveform inversionmachine learningdatumingPDF DownloadLoading ..." @default.
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• W3198178302 title "Target-oriented time-lapse elastic full-waveform inversion assisted by deep learning with prior information" @default.
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