Research on Coalbed Methane Production Forecasting Based on GCN-BiGRU Parallel Architecture—Taking Fukang Baiyanghe Mining Area in Xinjiang as an Example

Zhixin Jin, Kaiman Liu, Hongli Wang, Tong Liu (), Hongwei Wang, Xin Wang, Xuesong Wang, Lijie Wang, Qun Zhang and Hongxing Huang
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Zhixin Jin: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Kaiman Liu: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Hongli Wang: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Tong Liu: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Hongwei Wang: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Xin Wang: Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
Xuesong Wang: State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China
Lijie Wang: State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China
Qun Zhang: State Key Laboratory of Intelligent Mining Equipment Technology, Taiyuan 030032, China
Hongxing Huang: China United Coalbed Methane National Engineering Research Center Co., Ltd., Beijing 100095, China

Sustainability, 2025, vol. 17, issue 18, 1-35

Abstract: As a low-carbon and clean energy source, Coalbed methane (CBM) is of great significance in reducing greenhouse gas emissions, optimizing the energy structure, safeguarding mine safety, and promoting the transformation to a green economy to achieve sustainable development. Coalbed methane (CBM) in Xinjiang’s steeply dipping coal seams is abundant but difficult to predict due to complex geology and distinct gas flow behaviors, making traditional methods ineffective. This study proposes GCN-BiGRU, a parallel dual-module model integrating seepage mechanics, reservoir engineering, geological structures, and production history. The GCN module models wells as nodes, using geological attributes and spatial distances to capture inter-well interference; the BiGRU module extracts temporal dependencies from production sequences. An adaptive fusion mechanism dynamically combines spatiotemporal features for robust prediction. Validated on Baiyanghe block data, the model achieved MAE 59.04, RMSE 94.25, and improved accuracy from 64.47% to 92.8% as training wells increased from 20 to 84. It also showed strong transferability to independent sub-regions, enabling real-time prediction and scenario analysis for CBM development and reservoir management.

Keywords: dip angle; coalbed methane; production forecast; spatial–temporal features; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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