Evaluation of coal structure based on machine learning logging inversion: A case from NO.8 coal of Benxi Formation in Yulin area of Ordos Basin

LI An; CAI Yidong; WANG Zihao; LIU Dameng

doi:10.19509/j.cnki.dzkq.tb20240539

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LI An，CAI Yidong，WANG Zihao，et al. Evaluation of coal structure based on machine learning logging inversion: A case from NO.8 coal of Benxi Formation in Yulin area of Ordos Basin[J]. Bulletin of Geological Science and Technology，2025，44（4）：1-14 doi: 10.19509/j.cnki.dzkq.tb20240539

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Evaluation of coal structure based on machine learning logging inversion: A case from NO.8 coal of Benxi Formation in Yulin area of Ordos Basin

doi: 10.19509/j.cnki.dzkq.tb20240539

LI An^1
,,
CAI Yidong^{1, 2
,
,},
WANG Zihao¹,
LIU Dameng^{1, 2}

1.
School of Energy Resources, China University of Geosciences, Beijing 100083, China
2.
Beijing Key Laboratory of Unconventional Natural Gas Geological Evaluation and Development, Bejing 100083, China

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Author Bio:
E-mail：m18613918831@163.com
Corresponding author: E-mail：yidong.cai@cugb.edu.cn
Received Date: 19 Sep 2024
Accepted Date: 02 Jan 2025
Rev Recd Date: 30 Dec 2024

Available Online: 09 Jun 2025

Abstract

Abstract

Objective
Coal structure directly affects the pore and fracture system of coal reservoirs. Therefore, accurate identification of coal structure is crucial for guiding coal seam fracturing and coal bed methane extraction. Taking No. 8 coal of the Benxi Formation in the Yulin area of the Ordos Basin as an example, the coal structure is complex, necessitating the use of machine learning methods to address the nonlinear challenges in logging data interpretation.
Methods
In this study, Back Propagation Neural Network (BP), Random Forest, and XGBoost algorithms are trained on pre-processed core well data from the study area to perform coal structure inversion across the region. The analysis also considers the top and bottom plates of the coal seams and the coal thickness to explore the development of coal structure under tectonic control.
Results
The results indicate that: (1) Random Forest and XGBoost algorithms provide more accurate inversion results than the BP neural network, aligning more closely with real core observations. (2) The degree of coal structure fragmentation in No. 8 coal in the Yulin area increases progressively from northwest to southeast. (3) Tectonic zones, developed from the central to southeastern part of the study area, cause a reduction in coal thickness, with the coal structure transitioning from primary coal to mylonitic coal under tectonic influences.
Conclusion
The study can provide valuable insights for coal structure identification and tectonic zone analysis in coalbed methane production.
- Coal structure,
- machine learning,
- Back Propagation Neural Network,
- Random Forest,
- XGBoost,
- structural control,
- Ordos Basin,
- Yulin area

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References

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Evaluation of coal structure based on machine learning logging inversion: A case from NO.8 coal of Benxi Formation in Yulin area of Ordos Basin

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Evaluation of coal structure based on machine learning logging inversion: A case from NO.8 coal of Benxi Formation in Yulin area of Ordos Basin

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