Direct hydrocarbon indicators based on long short-term memory neural network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Direct hydrocarbon indicators based on long short-term memory neural networkAuthors: Luiz Fernando SantosReinaldo Mozart Gama E SilvaMarcelo GattassAristofanes Correa SilvaLuiz Fernando SantosComputer Science Department and Tecgraf/PUC-RIOSearch for more papers by this author, Reinaldo Mozart Gama E SilvaComputer Science Department and Tecgraf/PUC-RIOSearch for more papers by this author, Marcelo GattassComputer Science Department and Tecgraf/PUC-RIOSearch for more papers by this author, and Aristofanes Correa SilvaElectrical Engineering Department and NCA/UFMASearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215628.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDirect hydrocarbon indicators (DHIs) are a seismic anomaly that may indicate a possible reservoir. However, the task of finding a DHI anomaly in the seismic data is arduous considering that in most of the cases, this indicator is hard to sight in a large seismic data. Deep neural networks algorithms have been solving similar human-intensive and time-consuming problems with increasing speed and accuracy. In this paper, we propose a novel method to detect possible DHIs on seismic data applying a Long Short-Term Memory (LSTM) neural network on the seismic trace scale. To test our proposal, we use the Netherlands Offshore F3-Block, a known open 3D seismic data with a significant amount of gas. The results show that the proposed DHIs achieving a 97% accuracy, 95% sensitivity, 97% specificity, and 99% AUC.Presentation Date: Wednesday, September 18, 2019Session Start Time: 8:30 AMPresentation Time: 8:55 AMLocation: 221DPresentation Type: OralKeywords: artificial intelligence, gas, seismic attributesPermalink: https://doi.org/10.1190/segam2019-3215628.1FiguresReferencesRelatedDetailsCited byRecurrent autoencoder model for unsupervised seismic facies analysisYanhui Zhou and Wenchao Chen15 June 2022 | Interpretation, Vol. 10, No. 3Gas reservoir segmentation in 2D onshore seismics using LSTM-AutoEncoderFelipe Andrade, Luiz Fernando Santos, Marcelo Gattass, Roberto Quevedo, Diogo Michelon, Carlos Siedschlag, and Roberto Ribeiro1 September 2021Natural gas detection in onshore data using transfer learning from a LSTM pre-trained with offshore dataLuiz Fernando Santos, Marcelo Gattass, Aristofanes Correa Silva, Frederico Miranda, Carlos Siedschlag, and Roberto Ribeiro30 September 2020 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 Luiz Fernando Santos, Reinaldo Mozart Gama E Silva, Marcelo Gattass, and Aristofanes Correa Silva, (2019), "Direct hydrocarbon indicators based on long short-term memory neural network," SEG Technical Program Expanded Abstracts : 2373-2377. https://doi.org/10.1190/segam2019-3215628.1 Plain-Language Summary Keywordsartificial intelligencegasseismic attributesPDF DownloadLoading ...