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September 10, 202550th U.S. Rock Mechanics/Geomechanics Symposium

Intelligent Lithology Identification of Deep Coal Seam Driven by Drilling Parameters

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Authors

BYBo YuanSZS. C. ZhangCXCaiyun Xiao

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Overview

Observational analysis identifies lithology in coal seams using drilling parameters, indicating machine learning advantages.

Key Points

  • The Random Forest model achieves a lithology identification accuracy of 96.59%, enhancing drilling efficiency.
  • By integrating drilling parameters like bottomhole torque and mechanical specific energy, effective identification is realized.
  • This approach employs various machine learning models, improving classification of coal seam lithology substantially.
  • Machine learning models offer rapid computation, supporting precise reservoir evaluation and hydraulic fracturing applications.

Cite This Study

Yuan et al. (2025) studied this question.

synapsesocial.com/papers/68c1b60d54b1d3bfb60eb3c3https://doi.org/10.56952/arma-2025-0300
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Lithology Identification of Deep Coal Seam Based on Machine Learning2024 · 1 citations
  2. 2Real‐time lithology identification while drilling based on drill cuttings image analysis with ensemble learning2025
  3. 3Prediction andControlling Factors of Macrolithotypesin Deep Coal Reservoirs2026
  4. 4Quantitative Evaluation of Deep Coalbed Methane Content: A Logging Data‐Driven Approach2025
  5. 5Optimizing Reservoir Characterization with Machine Learning: Predicting Coal Texture Types for Improved Gas Migration and Accumulation Analysis2025