Prediction of coalbed methane production based on deep learning
Title
Prediction of coalbed methane production based on deep learning
Subject
Coalbed methane
Deep learning
Feature extraction
Long short-term memory
Production prediction
Description
Coalbed methane (CBM) is a clean energy source. The prediction of CBM production is a critical step during CBM exploitation and utilization, especially for geological well selection, engineering decision making, and production management. In past attempts, CBM production prediction methods have been limited to numerical simulation and shallow neural network. Compared with numerical simulation and shallow neural network methods, deep learning has a significant advantage in its ability to process big data with multiple sources and heterogeneity. Therefore, we developed a new method of CBM production prediction based on deep learning theory. The main novelties of this method are as follows. (1) A new feature extraction method for multiscale data sources is proposed by combining convolutional autoencoder and spatial pyramid pooling. (2) The CBM production prediction model based on deep learning is established by combining the affinity propagation (AP) algorithm and the long short-term memory (LSTM) network. Application and verification show that the accuracy of our new method is higher than that of the traditional numerical simulation and shallow neural network methods.
120847
230
Creator
Guo, Zixi
Zhao, Jinzhou
You, Zhenjiang
Li, Yongming
Zhang, Shu
Chen, Yiyu
Publisher
Energy
Date
2021
Type
journalArticle
Identifier
0360-5442
10.1016/j.energy.2021.120847
Citation
Guo, Zixi et al., “Prediction of coalbed methane production based on deep learning,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/27447.