Prediction of coalbed methane production based on deep learning

Zixi Guo, Jinzhou Zhao, Zhenjiang You, Yongming Li, Shu Zhang and Yiyu Chen

Energy, 2021, vol. 230, issue C

Abstract: 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.

Keywords: Coalbed methane; Production prediction; Deep learning; Feature extraction; Long short-term memory (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010951

DOI: 10.1016/j.energy.2021.120847

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