Intelligent analysis of pore structure for oil reservoir based on conditional GAN

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Intelligent analysis of pore structure for oil reservoir based on conditional GANAuthors: Yili RenHe LiuLu LuoJia LiangYan GaoYili RenResearch Institute of Petroleum Exploration & DevelopmentSearch for more papers by this author, He LiuResearch Institute of Petroleum Exploration & DevelopmentSearch for more papers by this author, Lu LuoResearch Institute of Petroleum Exploration & DevelopmentSearch for more papers by this author, Jia LiangResearch Institute of Petroleum Exploration & DevelopmentSearch for more papers by this author, and Yan GaoResearch Institute of Petroleum Exploration & DevelopmentSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3420478.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractReservoir pore structure is an important factor to control reservoir physical properties and productivity, and also an important basis for reservoir characterization. Core slice image is an important method of pore structure analysis. At present, the analysis of core slice image mainly depends on the traditional image processing technology, and uses expert experience as an assistant. This method can only process a single image one by one. It has a long analysis period, a slow speed, and no effective use of the historical data that has been analyzed. In this paper, a large number of core slice images are collected, and the pore structure analysis model is constructed by Conditional GAN network. When a core slice image is input into the model, it can automatically give the corresponding pore structure distribution map. In this way, the intelligent analysis of reservoir pore structure is realized by the method of deep learning. At the same time, we put forward C-IoU, C-MSE evaluation indexes, which effectively solve the difficulty of model evaluation in the application of Conditional GAN.Presentation Date: Monday, October 12, 2020Session Start Time: 1:50 PMPresentation Time: 2:15 PMLocation: Poster Station 1Presentation Type: PosterKeywords: artificial intelligence, machine learning, reservoir characterizationPermalink: https://doi.org/10.1190/segam2020-3420478.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Yili Ren, He Liu, Lu Luo, Jia Liang, and Yan Gao, (2020), "Intelligent analysis of pore structure for oil reservoir based on conditional GAN," SEG Technical Program Expanded Abstracts : 1601-1605. https://doi.org/10.1190/segam2020-3420478.1 Plain-Language Summary Keywordsartificial intelligencemachine learningreservoir characterizationPDF DownloadLoading ...