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Study on the Distribution Law of Coal Seam Gas and Hydrogen Sulfide Affected by Abandoned Oil Wells

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  • Xiaoqi Wang

    (College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China
    Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China)

  • Heng Ma

    (College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China
    Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China)

  • Xiaohan Qi

    (College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China
    Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China)

  • Ke Gao

    (College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China
    Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China)

  • Shengnan Li

    (College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China
    Key Laboratory of Mine Thermo-Motive Disaster and Prevention, Ministry of Education, Huludao 125105, China)

Abstract

This paper is devoted to solving the problem of how to comprehensively control coal seam gas and hydrogen sulfide in the mining face, distributed from the coal seam in abandoned oil wells in coal mining resource areas. The abandoned oil wells of Ma tan 30 and Ma tan 31 in the No. I0104 1 05 working face of the Shuang Ma Coal Mine were taken as examples. Through parameter testing, gas composition analysis, field investigation at the source distribution, and the influence range of gas and hydrogen sulfide in coal seam in the affected range of the abandoned oil wells were studied. The results show that the coal-bearing strata in Shuang Ma coal field belong to the coal–oil coexistence strata, and the emission of H 2 S gas in the local area of the working face is mainly affected by closed and abandoned oil wells. Within the influence range of the abandoned oil wells, along the direction of the working face, the concentration of CH 4 and H 2 S gas in the borehole increases as you move closer to the coal center, and the two sides of the oil well show a decreasing trend. In the affected area of the abandoned oil well, the distribution of the desorption gas content in coal seam along the center distance of the oil well presents a decreasing trend in power function, particularly the closer the working face is to the center of the oil well. The higher the concentration of CH 4 and H 2 S, the lower the concentration when the working face moves further away from the oil well. The influence radius of CH 4 and H 2 S gas on the coal seam in the affected area of Ma tan 31 abandoned oil well is over 300 m. The results provide a theoretical basis for further understanding the law of gas and hydrogen sulfide enrichment in the mining face and the design of treatment measures within the influence range of abandoned oil wells.

Suggested Citation

  • Xiaoqi Wang & Heng Ma & Xiaohan Qi & Ke Gao & Shengnan Li, 2022. "Study on the Distribution Law of Coal Seam Gas and Hydrogen Sulfide Affected by Abandoned Oil Wells," Energies, MDPI, vol. 15(9), pages 1-19, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3373-:d:809179
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    References listed on IDEAS

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    1. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
    2. Castillo, Enrique & Calviño, Aida & Lo, Hong K. & Menéndez, José María & Grande, Zacarías, 2014. "Non-planar hole-generated networks and link flow observability based on link counters," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 239-261.
    3. Rodriguez-Vega, Martin & Canudas-de-Wit, Carlos & Fourati, Hassen, 2019. "Location of turning ratio and flow sensors for flow reconstruction in large traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 21-40.
    4. Chad Zanocco & Hilary Boudet & Roberta Nilson & Hannah Satein & Hannah Whitley & June Flora, 2018. "Place, proximity, and perceived harm: extreme weather events and views about climate change," Climatic Change, Springer, vol. 149(3), pages 349-365, August.
    5. He, Sheng-xue, 2013. "A graphical approach to identify sensor locations for link flow inference," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 65-76.
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    Cited by:

    1. Longjun Dong & Yanlin Zhao & Wenxue Chen, 2022. "Mining Safety and Sustainability—An Overview," Sustainability, MDPI, vol. 14(11), pages 1-6, May.

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