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Sandstone porosity prediction based on gated recurrent units
HUI SONG1 ,
WEI CHEN*2,3 ,
HUA ZHANG4 ,
YANG WANG5,6 ,
YAJUAN XUE7
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1
College of Geophysics and Petroleum Resources, Yangtze University, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China. 201400567@yangtzeu.edu.cn,
2
Key Laboratory of Exploration Technology for Oil and Gas Resources of Ministry of Education, Yangtze University, Daxue Road 111, Wuhan 430100, P.R. China.,
3
Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China.,
4
School of Geophysics and Measurement-control Technology, East China University of Technology, Nanchang 330013, P.R. China.,
5
Faculty of Materials Science & Engineering, Hubei University, Wuhan 430062, China.,
6
Tianshu New Energy Material Industry Research and Design Institute, Hubei University, Wuhan 430062, P.R. China.,
7
School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, P.R. China.,
JSE 2020, 29(4), 371–388;
Submitted: 1 March 2019 | Accepted: 28 February 2020 | Published: 1 August 2020
© 2020 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
Song, H., Chen, W., Zhang, H., Wang, Y. and Xue, Y.J., 2020. Sandstone porosity prediction based on gated recurrent units. Journal of Seismic Exploration, 29: 371-388. Sandstone porosity prediction is a difficult task because of the heterogeneity of reservoir rock. Deep learning has been widely used in various fields, but it is rarely used for sandstone porosity prediction. Recurrent neural networks (RNNs) are currently very popular algorithms for deep learning and have achieved good performance in processing sequence data, such as speech recognition and machine translation. Since sandstone porosity prediction belongs to sequence data prediction, this paper proposes to use the gated recurrent units (GRUs), which is a variant of RNNs, to predict sandstone porosity using the well logs data. Six well logs data are divided into training set, validation set and testing set. We apply three deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and gated recurrent units (GRUs) to predict sandstone porosity. Results show that GRUs can extract the nonlinear characteristics of data more effectively and are more suitable for sandstone porosity prediction.
Keywords
convolutional neural networks (CNNs)
recurrent neural networks (RNNs)
gated recurrent units (GRUs)
sandstone porosity prediction
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