Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the well

Abstract

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyAutomatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the wellAuthors: Paige GivenFantine HuotAriel LellouchBin LuoRobert G. ClappBiondo L. BiondiTamas NemethKurt NiheiPaige GivenStanford UniversitySearch for more papers by this author, Fantine HuotStanford UniversitySearch for more papers by this author, Ariel LellouchStanford UniversitySearch for more papers by this author, Bin LuoStanford UniversitySearch for more papers by this author, Robert G. ClappStanford UniversitySearch for more papers by this author, Biondo L. BiondiStanford UniversitySearch for more papers by this author, Tamas NemethChevron Technical CenterSearch for more papers by this author, and Kurt NiheiChevron Technical CenterSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751887.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDownhole Distributed Acoustic Sensing (DAS) fibers lie within close proximity to microseismic events, and thus are able to detect events with very high resolution and accuracy. With this high resolution comes a substantial amount of data several terabytes (TB) in size over a few days worth of record- ing. We created a convolutional neural network that can automatically detect microseismic events in DAS data. Prior work (Lellouch et al., 2021; Huot et al., 2021b), displayed the results of our network when tested on DAS data taken during well-stimulation phases. Here, we run our algorithm through 2-hours worth of data collected approximately one week after stimulation of the well has ceased, and compare the results to previous single-staged results. Our network detected events in approximately 2.04% of the data windows in the 2-hour post- stimulation phase, where previously it had predicted 3.67% of the data to hold events when tested on a single collection stage during well-stimulation. By analyzing the microseismic events in our data post-stimulation, we can analyze fracture evolution in time.Keywords: machine learning, DAS, unconventional, CNN, fracturePermalink: https://doi.org/10.1190/image2022-3751887.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Paige Given, Fantine Huot, Ariel Lellouch, Bin Luo, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, and Kurt Nihei, (2022), "Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the well," SEG Technical Program Expanded Abstracts : 1966-1969. https://doi.org/10.1190/image2022-3751887.1 Plain-Language Summary Keywordsmachine learningDASunconventionalCNNfracturePDF DownloadLoading ...