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Feasibility investigation of enhanced coalbed methane recovery by steam injection

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  • Li, Yujie
  • Zhai, Cheng
  • Xu, Jizhao
  • Sun, Yong
  • Yu, Xu

Abstract

Heat injection is an effective technique to enhance unconventional oil and gas recovery. As an excellent heat transfer medium, steam has a great potential for coalbed methane (CBM) recovery enhancement. To investigate the feasibility of enhanced CBM recovery by steam injection, infrared thermal imaging (ITI) and thermocouples were employed to study the heating efficiency of steam on three ranks of coal, and nuclear magnetic resonance (NMR) was applied to analyze water transport within the coal after steam treatment. The results showed that the internal temperature of the coal samples reached about 80 °C in 17 min of steam treatment and thermal cracking of coal were induced, hence steam injection could facilitate gas desorption and provide more channels for gas migration. NMR test results indicated that steam preferentially condensed in coal micropores due to the capillary condensation effect, hence steam might have the potential to displace methane. The T2 spectra of the coals after steam treatment exhibited only the first two peaks without the third peak, indicating that steam injection might not cause the water blocking effect. The outcomes of this study are expected to provide valuable insights into the field application of steam injection to enhance CBM production.

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  • Li, Yujie & Zhai, Cheng & Xu, Jizhao & Sun, Yong & Yu, Xu, 2022. "Feasibility investigation of enhanced coalbed methane recovery by steam injection," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222013767
    DOI: 10.1016/j.energy.2022.124473
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    5. Zhang, Hewei & Shen, Jian & Wang, Geoff & Li, Kexin & Fang, Xiaojie & Jing, Qu, 2023. "Differential heat transfer characteristics of coal macerals and their control mechanism: At the mesoscale," Energy, Elsevier, vol. 280(C).
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    7. Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).

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