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<p>Multi-scale mechanical research has become an effective approach for analyzing the behavior of complex materials, enabling the prediction of macroscopic properties from microscale characteristics. Pyrite, a key mineral affecting the mechanical properties of shale, has attracted considerable attention due to its brittleness and distinctive microstructure. However, the specific effects of pyrite’s microscopic features on shale mechanics have not been systematically investigated. In this study, Python-based machine learning techniques are employed to establish an automated framework for quantitatively analyzing pyrite’s microscopic characteristics. The influence of pyrite content, particle size, shape factor, and distribution on shale mechanical properties and fracture network formation is examined using a random forest regression algorithm and numerical simulations. The results show that pyrite content exerts the strongest influence on shale mechanics, followed by shape factor, while particle size and distribution mode have comparatively weaker effects. Compressive strength and fracture behavior are particularly sensitive to elevated pyrite content and shape factor. All four factors (pyrite content, particle size, shape factor, and particle distribution) affect mechanical properties through stress concentration, with shape factor additionally governing particle interlocking. Distribution mode further modulates the mechanical response by influencing the formation of force chain networks. Numerical simulations reveal that fracture network development is optimized when pyrite content is 3.00% and the shape factor is 0.81, thereby enhancing the fracturing effect. This study provides theoretical support for hydraulic fracturing in shale reservoirs and introduces a novel perspective on the role of pyrite’s microscopic characteristics in governing shale mechanical behavior.</p>

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Impact of pyrite microscopic features on shale mechanical properties: a machine learning and simulation study

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Xiaofan Mei,
  • Shuheng Tang,
  • Zhaodong Xi,
  • Yapei Ye,
  • Qian Zhang,
  • Yang Chen,
  • Donglin Lin,
  • Xiongxiong Yang

摘要

Multi-scale mechanical research has become an effective approach for analyzing the behavior of complex materials, enabling the prediction of macroscopic properties from microscale characteristics. Pyrite, a key mineral affecting the mechanical properties of shale, has attracted considerable attention due to its brittleness and distinctive microstructure. However, the specific effects of pyrite’s microscopic features on shale mechanics have not been systematically investigated. In this study, Python-based machine learning techniques are employed to establish an automated framework for quantitatively analyzing pyrite’s microscopic characteristics. The influence of pyrite content, particle size, shape factor, and distribution on shale mechanical properties and fracture network formation is examined using a random forest regression algorithm and numerical simulations. The results show that pyrite content exerts the strongest influence on shale mechanics, followed by shape factor, while particle size and distribution mode have comparatively weaker effects. Compressive strength and fracture behavior are particularly sensitive to elevated pyrite content and shape factor. All four factors (pyrite content, particle size, shape factor, and particle distribution) affect mechanical properties through stress concentration, with shape factor additionally governing particle interlocking. Distribution mode further modulates the mechanical response by influencing the formation of force chain networks. Numerical simulations reveal that fracture network development is optimized when pyrite content is 3.00% and the shape factor is 0.81, thereby enhancing the fracturing effect. This study provides theoretical support for hydraulic fracturing in shale reservoirs and introduces a novel perspective on the role of pyrite’s microscopic characteristics in governing shale mechanical behavior.