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<p>Natural fractures play a crucial role in controlling shale reservoir productivity, emphasizing the need for thorough characterization and predictive modeling to improve hydrocarbon extraction efficiency. In this study, a comprehensive workflow integrating borehole core observations, imaging logging interpretations, and data-driven predictive modeling was employed to investigate natural fracture systems in the Longmaxi Formation shale gas reservoir located in the Luzhou area of the southern Sichuan Basin. First, the mechanical types, filling degree, and occurrence characteristics of natural fractures were quantitatively characterized. Subsequently, leveraging these insights, a neural network model trained on conventional logging data was developed to predict fracture intensity in individual wells, demonstrating broad applicability where imaging log data are limited. Furthermore, correlation analysis identified tectonic activity, curvature attributes, and rock mineral composition as the primary factors influencing fracture development. Finally, by integrating multi-attribute fracture intensity probability volumes (with spatial constraints) and statistical analyses of dip-azimuth data, a 3D multiscale fracture model was constructed, which achieved a prediction error rate of 7.32–18.18% against observed fracture networks. These findings clarify the development characteristics of natural fractures in the Longmaxi Formation and establish a predictive framework for fracture modeling in deep, structurally complex shale reservoirs.</p>

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Development characteristics and distribution prediction of natural fractures within deep shale gas reservoirs of Luzhou area, southern Sichuan Basin

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Weike Ning,
  • Wei Ju,
  • Yuyang Liu,
  • Yu Wang,
  • Wei Guo

摘要

Natural fractures play a crucial role in controlling shale reservoir productivity, emphasizing the need for thorough characterization and predictive modeling to improve hydrocarbon extraction efficiency. In this study, a comprehensive workflow integrating borehole core observations, imaging logging interpretations, and data-driven predictive modeling was employed to investigate natural fracture systems in the Longmaxi Formation shale gas reservoir located in the Luzhou area of the southern Sichuan Basin. First, the mechanical types, filling degree, and occurrence characteristics of natural fractures were quantitatively characterized. Subsequently, leveraging these insights, a neural network model trained on conventional logging data was developed to predict fracture intensity in individual wells, demonstrating broad applicability where imaging log data are limited. Furthermore, correlation analysis identified tectonic activity, curvature attributes, and rock mineral composition as the primary factors influencing fracture development. Finally, by integrating multi-attribute fracture intensity probability volumes (with spatial constraints) and statistical analyses of dip-azimuth data, a 3D multiscale fracture model was constructed, which achieved a prediction error rate of 7.32–18.18% against observed fracture networks. These findings clarify the development characteristics of natural fractures in the Longmaxi Formation and establish a predictive framework for fracture modeling in deep, structurally complex shale reservoirs.