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<p>The geological setting of the Damodar River valley basin provides scope for the generation of high coal bed methane (CBM) gas within coal seams. However, the current production rate and CBM exploration activity in this basin are insufficient to meet requirements. Primarily, two Permian Formations, such as Raniganj and Barakar, in the Lower Gondwana age produce CBM gas. The current study is restricted to the Barakar Formation of the Jharia coalfield region. The presence of CBM gas from the major coal seams in this region has already been established, with production underway. However, a few potential thin coal seams, including geological challenges associated with the major coal seams, have not been identified. The limited availability of geoscientific data makes these scenarios more challenging. This study has overcome these challenges by developing higher-quality images towards revealing a detailed understanding of the subsurface geology. The introduction of full-waveform inversion (FWI) and the study of machine learning (ML) highlight the key role of this study. The advanced processing sequence, such as FWI, shows key improvements in seismic features. Land gravity data were used in this study to develop a sedimentary model, whereas the ML technique was used to generate well log responses at strategically selected pseudo-well positions, along with the generation and calibration of missing log responses. A robust integrated geo-cellular model was developed, showing that the study area contains many potential unexplored coal seams for CBM exploration, with a few of them thin.</p>

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Analysis of Geostatistical Model to Identify the Potential of Challenging Coal Bed Methane (CBM) Reservoir with the Support of Machine Learning (ML) and Synthetically Generated Seismic Data: A Study from Jharia Coalfield of the Damodar River Valley Basin, India

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Saurabh Datta Gupta,
  • Sugata Kumar Sinha

摘要

The geological setting of the Damodar River valley basin provides scope for the generation of high coal bed methane (CBM) gas within coal seams. However, the current production rate and CBM exploration activity in this basin are insufficient to meet requirements. Primarily, two Permian Formations, such as Raniganj and Barakar, in the Lower Gondwana age produce CBM gas. The current study is restricted to the Barakar Formation of the Jharia coalfield region. The presence of CBM gas from the major coal seams in this region has already been established, with production underway. However, a few potential thin coal seams, including geological challenges associated with the major coal seams, have not been identified. The limited availability of geoscientific data makes these scenarios more challenging. This study has overcome these challenges by developing higher-quality images towards revealing a detailed understanding of the subsurface geology. The introduction of full-waveform inversion (FWI) and the study of machine learning (ML) highlight the key role of this study. The advanced processing sequence, such as FWI, shows key improvements in seismic features. Land gravity data were used in this study to develop a sedimentary model, whereas the ML technique was used to generate well log responses at strategically selected pseudo-well positions, along with the generation and calibration of missing log responses. A robust integrated geo-cellular model was developed, showing that the study area contains many potential unexplored coal seams for CBM exploration, with a few of them thin.