MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||6|
|Section||Parallel Session II: Water System Technology|
|Published online||07 December 2018|
Application of deep learning for division of petroleum reservoirs
1 Department of Automation, Tsinghua University, Beijing 100084, China
2 China Oilfield Services Limited, Sanhe, Hebei 065201, China
a Corresponding author: email@example.com
Traditional methods of dividing petroleum reservoirs are inefficient, and the accuracy of onehidden-layer BP neural network is not ideal when applied to dividing reservoirs. This paper proposes to use the deep learning models to solve the reservoir division problem. We apply multiple-hidden-layer BP neural network and convolutional neural network models, and adjust the network structures according to the characteristics of the reservoir problem. The results show that the deep learning models are better than onehidden- layer BP neural network, and the performance of the convolutional neural network is very close to the artificial work.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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