Attenuation-based deep learning for volcanic reservoirs characterization: A case study from Junggar Basin, China

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsAttenuation-based deep learning for volcanic reservoirs characterization: A case study from Junggar Basin, ChinaAuthors: Xiaoming SunXinming WuSiyuan CaoXiaoming SunComputational Interpretation Group and the University of Science and Technology of ChinaSearch for more papers by this author, Xinming WuComputational Interpretation Group and the University of Science and Technology of ChinaSearch for more papers by this author, and Siyuan CaoChina University of Petroleum (Beijing)Search for more papers by this authorhttps://doi.org/10.1190/segam2021-3588888.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic characterization of volcanic reservoirs is a difficult task considering the low quality of seismic data. The reflection of volcanic rocks is always characterized by high amplitude and relatively low frequency compared with reflections of clastic sediments which makes the identification of oil-bearing volcanic reservoirs more difficult. In order to depict volcanic reservoirs in Junggar Basin, we combine time frequency analysis and deep learning to predict the Carboniferous volcanic reservoirs. We first use time frequency analysis to determine the features of volcanic reservoirs’ reflection and the specific frequency for characterizing volcanic reservoirs. Second, we use low-, middle-, and high-frequency component and its corresponding attributes as Convolutional Neural Networks’ input to predict the impedance of volcanic rocks. The results are in line with the drilling data, and this method would give insights to characterize volcanic reservoirs in Junggar Basin.Keywords: reservoir characterization, inversion, machine learningPermalink: https://doi.org/10.1190/segam2021-3588888.1FiguresReferencesRelatedDetails 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 Xiaoming Sun, Xinming Wu, and Siyuan Cao, (2021), "Attenuation-based deep learning for volcanic reservoirs characterization: A case study from Junggar Basin, China," SEG Technical Program Expanded Abstracts : 2124-2128. https://doi.org/10.1190/segam2021-3588888.1 Plain-Language Summary Keywordsreservoir characterizationinversionmachine learningPDF DownloadLoading ...