Predicting carbonate permeabilities from wireline logs using a back‐propagation neural network

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 1991Predicting carbonate permeabilities from wireline logs using a back‐propagation neural networkAuthors: Jack M. WienerJohn A. RogersJohn R. RogersRobert F. MollJack M. WienerTexaco E&P, John A. RogersM.I.T., John R. RogersTexaco E&P, and Robert F. MollTexaco E&Phttps://doi.org/10.1190/1.1888943 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1888943FiguresReferencesRelatedDetailsCited byIntegration of Seismic Attributes with Well Logs Using Artificial Neural Network for Predicting the Key Pay Zones Parameters: A Case Study in the Kifl Field, South of Iraq16 August 2022 | Arabian Journal of Geosciences, Vol. 15, No. 16Relative Permeability Modeling Using Extra Trees, ANFIS, and Hybrid LSSVM–CSA Methods13 November 2021 | Natural Resources Research, Vol. 31, No. 1Contrasting machine learning regression algorithms used for the estimation of permeability from well log data26 September 2021 | Arabian Journal of Geosciences, Vol. 14, No. 20Seismic inversion via closed-loop fully convolutional residual network and transfer learningLingling Wang, Delin Meng, and Bangyu Wu12 August 2021 | GEOPHYSICS, Vol. 86, No. 5Vertical lithological proxy using statistical and artificial intelligence approach: a case study from Krishna-Godavari Basin, offshore India4 January 2021 | Marine Geophysical Research, Vol. 42, No. 1Assisted History Matching of a Highly Heterogeneous Carbonate Reservoir Using Hydraulic Flow Units and Artificial Neural Networks1 December 2020Convolutional neural network for seismic impedance inversionVishal Das, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji9 October 2019 | GEOPHYSICS, Vol. 84, No. 6Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)15 July 2019 | Journal of Earth System Science, Vol. 128, No. 7Prestack and poststack inversion using a physics-guided convolutional neural networkReetam Biswas, Mrinal K. Sen, Vishal Das, and Tapan Mukerji15 July 2019 | Interpretation, Vol. 7, No. 3Permeability prediction from wireline logging and core data: a case study from Assam-Arakan basin4 June 2018 | Journal of Petroleum Exploration and Production Technology, Vol. 9, No. 1Simultaneous Interpretation of Relative Permeability and Capillary Pressure for a Naturally Fractured Carbonate Formation from Wireline Formation Testing25 October 2019Convolutional neural network for seismic impedance inversionVishal Das, Ahinoam Pollack, Uri Wollner, and Tapan Mukerji27 August 2018Fracture density estimation from core and conventional well logs data using artificial neural networks: The Cambro-Ordovician reservoir of Mesdar oil field, AlgeriaJournal of African Earth Sciences, Vol. 83Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB applicationComputers & Geosciences, Vol. 35, No. 11Predicting formation lithology from log data by using a neural network7 August 2008 | Petroleum Science, Vol. 5, No. 3Neural Network Based Interpretation Algorithm for Combined Induced Polarization and Vertical Electrical Soundings of Coastal ZonesRambhatla G. Sastry and Haile G. Tesfakiros21 June 2012 | Journal of Environmental and Engineering Geophysics, Vol. 11, No. 3The application of artificial neural networks to magnetotelluric time-series analysisGeophysical Journal International, Vol. 153, No. 2Neural network interpretation of LWD data (ODP Leg 170) confirms complete sediment subduction at the Costa Rica convergent marginEarth and Planetary Science Letters, Vol. 174, No. 3-4Estimating one-dimensional models from frequency-domain electromagnetic data using modular neural networksIEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 2Lithofacies Identification from Wireline Logs10 April 2012 SEG Technical Program Expanded Abstracts 1991ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 1991 Pages: 1646 publication data© 1991 Copyright © 1991 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Feb 2005 CITATION INFORMATION Jack M. Wiener, John A. Rogers, John R. Rogers, and Robert F. Moll, (1991), "Predicting carbonate permeabilities from wireline logs using a back‐propagation neural network," SEG Technical Program Expanded Abstracts : 285-288. https://doi.org/10.1190/1.1888943 Plain-Language Summary PDF DownloadLoading ...