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<p>Based on reservoir logging data and post-fracturing oil test data, the geomechanical characteristics of deep continental shale reservoirs from the Lower Permian Fengcheng Formation in the Mahu Sag were analyzed, and the main controlling factors of geomechanical influencing the fracturing effect were determined using the gray relation analysis (GRA) method. Thus, a fracability index model was developed based on production data and combination weighting derived from game theory. Based on the results, an intelligent prediction method for rock mechanical parameters was constructed based on the XGBoost method. Accordingly, using the GRA method, the main controlling factors of geomechanical effects on the fracturing effect were defined, including the brittleness index, horizontal stress difference, minimum horizontal principal stress, tensile strength, and uniaxial compressive strength. Based on production data, subjective weights (Analytic Hierarchy Process) and objective weights (Entropy Weight) were established. Game theory was then employed as a collaborative optimization strategy to reconcile these weights, creating a dynamic allocation method for key parameter contributions. The constructed fracability index exhibited a strong positive correlation with liquid production intensity.</p>

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Fracability evaluation of deep continental shale oil reservoirs based on combination weighting with game theory: a case study in the Lower Permian Fengcheng Formation in Mahu Sag, Junggar Basin

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Jian Xiong,
  • Ziqiang Zhang,
  • Xiangjun Liu,
  • Renzhong Gan,
  • Lixi Liang,
  • Tao Zhang,
  • Yi Ding

摘要

Based on reservoir logging data and post-fracturing oil test data, the geomechanical characteristics of deep continental shale reservoirs from the Lower Permian Fengcheng Formation in the Mahu Sag were analyzed, and the main controlling factors of geomechanical influencing the fracturing effect were determined using the gray relation analysis (GRA) method. Thus, a fracability index model was developed based on production data and combination weighting derived from game theory. Based on the results, an intelligent prediction method for rock mechanical parameters was constructed based on the XGBoost method. Accordingly, using the GRA method, the main controlling factors of geomechanical effects on the fracturing effect were defined, including the brittleness index, horizontal stress difference, minimum horizontal principal stress, tensile strength, and uniaxial compressive strength. Based on production data, subjective weights (Analytic Hierarchy Process) and objective weights (Entropy Weight) were established. Game theory was then employed as a collaborative optimization strategy to reconcile these weights, creating a dynamic allocation method for key parameter contributions. The constructed fracability index exhibited a strong positive correlation with liquid production intensity.