Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsSupervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoirAuthors: Tobi OreDengliang GaoTobi OreWest Virginia UniversitySearch for more papers by this author and Dengliang GaoWest Virginia UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594773.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn unconventional reservoir sweet-spot identification, brittleness is an important proxy used as a measure of easiness for oil and gas production. Production from this low permeability reservoir is realized by hydraulic fracturing, which depends on how brittle the rock is—as it opens natural fractures and also creates new fractures. An estimate of brittleness, brittleness index, is obtained at well locations through a mathematical combination of elastic logs. In practice, problems arise to predict brittleness because of the limited availability of elastic logs and sparsity of wells to understand the lateral variation of brittleness. To address this problem, machine learning techniques are adopted to predict brittleness at well locations from readily available geophysical logs and spatially using continuous 3D seismic data. This study tests machine learning algorithms to forecast reservoir brittleness throughout the entire reservoir interval using well logs and 3D seismic in a shale gas field of central Appalachian Basin. Our results show the effectiveness of using gradient boosting to predict brittleness from gamma ray, density, and neutron logs with a training and testing R2 score of 0.95 and 0.85, respectively. We demonstrate a novel application of seismic texture as an indicator for brittleness through the qualitative agreement of the inversion output with the blind well and also the fracture attribute.Keywords: machine learning, seismic attributes, reservoir characterization, unconventional, geomechanicsPermalink: https://doi.org/10.1190/segam2021-3594773.1FiguresReferencesRelatedDetails 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 Tobi Ore and Dengliang Gao, (2021), "Supervised machine learning to predict brittleness using well logs and seismic signal attributes: Methods and application in an unconventional reservoir," SEG Technical Program Expanded Abstracts : 1566-1570. https://doi.org/10.1190/segam2021-3594773.1 Plain-Language Summary Keywordsmachine learningseismic attributesreservoir characterizationunconventionalgeomechanicsPDF DownloadLoading ...