N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms

Kai Wang, Haoran Gong, Gongda Wang, Xin Yang, Haiteng Xue, Feng Du and Zhie Wang

Energy, 2024, vol. 290, issue C

Abstract: N2 injection to enhance coal seam gas drainage is an effective technology for coalbed methane recovery. Nevertheless, there is a limited body of research addressing the application of ECBM technology for gas management in low permeability coal seams within underground coal mines, and the effect is uncertain. Additionally, numerical simulators have been challenged to accurately predict methane emissions during gas injection and replacement. In this study, firstly, an underground field trials of N2 injection replacement in a coal mine with low-permeability coal seams in China were conducted, and a comparative analysis of “N2 injection to enhance gas drainage” and “conventional drainage” technique was performed. The result show that injecting pressurized N2 into a low-permeability coal seam could increase the pressure gradient to promote the directional flow of free gas from the coal seam cracks to the gas-production boreholes. After N2 injection, the mixed flow rate and CH4 flow rate significantly increased. Additionally, the gas content in the coal seam in the experimental area significantly decreased. Secondly, the lag-effect was discussed. Upon discontinuation of N2 injection, the gas-producing mixed flow initially increased slightly, then gradually decreased, and eventually settled at a high level. Despite the attenuation of N2 injection volume, the CH4 flow rate continued to increase during intermittent injection and remained high after gas injection discontinuation. Finally, three deep learning algorithms (BP, LSTM, and TCN) were employed to predict the dynamic change of gas drainage in coal seams displaced by N2 injection, based on the results of the field test. The results indicated that deep learning algorithms exhibited superior prediction performance. The TCN model demonstrated the lowest approximation error rate, displaying optimal performance in predicting the CH4 flow rate during nitrogen injection for enhanced gas drainage. Additionally, it exhibited good applicability to other similar field test results.

Keywords: Underground displacement by injecting N2; N2 injection to enhance gas drainage; Deep learning algorithm; Model optimization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223034047

DOI: 10.1016/j.energy.2023.130010

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