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• W3198633411 abstract "PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsStratigraphic automatic correlation using SegNet semantic segmentation modelAuthors: Yue DaiXuri HuangHaojie LiuHongwei YangGuohua WeiNing LuZhiying HanHaibo SongYue DaiSouthwest Petroleum UniversitySearch for more papers by this author, Xuri HuangSouthwest Petroleum UniversitySearch for more papers by this author, Haojie LiuSinopec Shengli Oilfield CompanySearch for more papers by this author, Hongwei YangSinopec Shengli Oilfield CompanySearch for more papers by this author, Guohua WeiSinopec Shengli Oilfield CompanySearch for more papers by this author, Ning LuSinopec Shengli Oilfield CompanySearch for more papers by this author, Zhiying HanSinopec Shengli Oilfield CompanySearch for more papers by this author, and Haibo SongBeijing Sunrise PetroSolutions Tech., LtdSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3584720.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractA multi-task encoder-decoder based on SegNet architecture is proposed for automatic stratigraphic correlation in this work. In order to have higher resolution correlation, logs and their wavelet transformed results are combined to form the training datasets. In addition, two types of loss functions for the SegNet are used to achieve high-resolution results. By applying this method to a field in Shengli Oilfield, the result demonstrates that this network can obtain accurate stratigraphic correlation and is significantly efficient compared to the conventional manual method. Using the correlated results and combined with dip attribute from seismic data, an isochronal stratigraphic framework is built for geological modeling and study. This work demonstrates that SegNet can be a reliable automatic well log correlation technique with high efficiency and accuracy.Keywords: well-log, interpretation, machine learning, modelingPermalink: https://doi.org/10.1190/segam2021-3584720.1FiguresReferencesRelatedDetails 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 Yue Dai, Xuri Huang, Haojie Liu, Hongwei Yang, Guohua Wei, Ning Lu, Zhiying Han, and Haibo Song, (2021), Stratigraphic automatic correlation using SegNet semantic segmentation model, SEG Technical Program Expanded Abstracts : 1591-1595. https://doi.org/10.1190/segam2021-3584720.1 Plain-Language Summary Keywordswell-log interpretationmachine learningmodelingPDF DownloadLoading ..." @default.
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• W3198633411 title "Stratigraphic automatic correlation using SegNet semantic segmentation model" @default.
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