Volume 43 Issue 4
Oct.  2021
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TANG Ying, LI Lezhong, JIANG Shixin, ZHANG Binhai, ZHONG Mihong, SUN Yuhong. Parameter selection and applicability of gas content logging interpretation methodology in coal seam[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(4): 94-98. DOI: 10.3969/j.issn.1001-1986.2015.04.020
Citation: TANG Ying, LI Lezhong, JIANG Shixin, ZHANG Binhai, ZHONG Mihong, SUN Yuhong. Parameter selection and applicability of gas content logging interpretation methodology in coal seam[J]. COAL GEOLOGY & EXPLORATION, 2015, 43(4): 94-98. DOI: 10.3969/j.issn.1001-1986.2015.04.020
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Parameter selection and applicability of gas content logging interpretation methodology in coal seam

  • 1. School of Energy Resources, China University of Geosciences, Beijing 100083, China;

  • 2. Research and Development Center, CNOOC Gas&Power Group, Beijing 100027, China;

  • 3. CNOOC Research Institute, Beijing 100027, China;

  • 4. CNOOC Energy Technology&Service-Oilfield Technology Services Co. Drilling and Production Engineering Research Institute, Tianjin 300457, China

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  • Received Date: October 13, 2013
  • Available Online: October 21, 2021
    Graphical Abstract
    • Abstract
  • Multiple linear regression and BP neural network are gas content logging interpretation methodologies commonly used in coal seam. Based on well logging data and measured gas content of CBM well in Galilee basin of Australia and Qinshui basin of China, this study screened the logging related parameters of gas content through correlation analysis and then established the relationship model between gas content and logging parameters. Based on BP neural network theory, this study not only established a nonlinear prediction model of CBM gas content and logging parameters through the network training and prediction, but also analyzed the error of the two methods and discussed their applicability.
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