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• W2968782698 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Porosity evaluation of igneous rocks based on deep learningAuthors: Yunlong GeWensheng WuRuigang WangLiuqiong HeYunlong GeChina University of Petroleum-BeijingSearch for more papers by this author, Wensheng WuChina University of Petroleum-BeijingSearch for more papers by this author, Ruigang WangChina University of Petroleum-BeijingSearch for more papers by this author, and Liuqiong HeChina University of Petroleum-BeijingSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3211813.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe mineral composition of the igneous rock is complex. Even with the same lithology, the rock matrix parameters vary greatly, which makes it difficult to obtain the porosity accurately. In this paper, the formation porosity is obtained based on the density logging and the geochemical spectroscopy logging. The geochemical spectroscopy logging combined with deep learning is used to determine the rock matrix density. The deep learning database, constrained by the core analysis data, is established based on the physical properties of minerals and the Monte Carlo method. To make full use of the robustness of deep learning and improve the interpretation accuracy, the density of each mineral changes within a certain range when the rock matrix density of training samples is set up. Once the rock matrix density is obtained, porosity can be derived by the volume model. The field example shows the porosity calculated by deep learning is closer to the core porosity than that calculated by the conventional methods.Presentation Date: Tuesday, September 17, 2019Session Start Time: 9:20 AMPresentation Time: 9:20 AMLocation: Poster Station 4Presentation Type: PosterKeywords: machine learning, well-log interpretation, reservoir characterization, borehole geophysics, log analysisPermalink: https://doi.org/10.1190/segam2019-3211813.1FiguresReferencesRelatedDetailsCited byQuantitative characterization of shale gas reservoir properties based on BiLSTM with attention mechanismGeoscience Frontiers, Vol. 7Modeling extra-deep electromagnetic logs using a deep neural networkSergey Alyaev, Mostafa Shahriari, David Pardo, Ángel Javier Omella, David Selvåg Larsen, Nazanin Jahani, and Erich Suter13 May 2021 | GEOPHYSICS, Vol. 86, No. 3Research on well logging evaluation method of igneous reservoir in Nanpu No.5 structure29 July 2020 | Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Vol. 3The Phenomenon of existence Batu Angus on the eastern slopes of mount Gamalama Ternate island North MalukuJournal of Physics: Conference Series, Vol. 1511, No. 1 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Yunlong Ge, Wensheng Wu, Ruigang Wang, and Liuqiong He, (2019), Porosity evaluation of igneous rocks based on deep learning, SEG Technical Program Expanded Abstracts : 910-914. https://doi.org/10.1190/segam2019-3211813.1 Plain-Language Summary Keywordsmachine learningwell-log interpretationreservoir characterizationborehole geophysicslog analysisPDF DownloadLoading ..." @default.
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