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<p>Understanding lithology and flow unit characterization is essential for effective reservoir evaluation, particularly in complex clastic systems such as the Lower Cretaceous Yageliemu Formation of the Yakela gas condensate field, Kuqa Depression, Tarim Basin, China. This study integrates traditional crossplot techniques with machine learning-based lithology classification and petrophysical evaluation to characterize reservoir heterogeneity. Mineralogical analysis using M-N crossplots identifies a dominantly quartz-rich clastic matrix with subordinate shale content, and this result establishes the compositional basis for lithological interpretation. Lithological classification uses mineralogical constraints and applies diagnostic crossplots and K-means clustering to define four facies, which include clean sandstone, clayey sandstone, shaly sandstone, and shale. This classification provides a framework for evaluating petrophysical variations across reservoir facies. Petrophysical analysis shows significant differences in reservoir quality among facies. Clean sandstone (HFU-01) exhibits the highest reservoir quality with effective porosity of 9.9%, permeability of 10 mD, and favorable flow parameters (RQI = 0.20, FZI = 1.47), which indicate strong flow capacity. In contrast, shale (HFU-04) displays poor reservoir quality with porosity of 1.6%, permeability of 0.002 mD, and low flow potential (RQI = 0.008, FZI = 0.433). Clayey and shaly sandstones show intermediate properties that reflect transitional flow behavior. Gas-bearing intervals across four wells show effective porosity of 4.6–9.8%, permeability of 3.0-3.6 mD, shale volume of 19–24%, and gas saturation of 48–60%, which indicate moderate reservoir heterogeneity controlled by facies distribution. This integrated workflow links mineralogical, lithological, and petrophysical analyses and provides a framework for lithology classification and flow unit characterization. The study improves understanding of reservoir heterogeneity and enhances the reliability of flow unit delineation in clastic reservoir systems.</p>

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Integrated lithology classification using traditional and machine learning approaches with petrophysical properties evaluation for flow unit characterization

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Hadi Hussain,
  • Lin Pan,
  • Weiqi Li,
  • Xiao Wang,
  • Wakeel Hussain,
  • Sartaj Hussain

摘要

Understanding lithology and flow unit characterization is essential for effective reservoir evaluation, particularly in complex clastic systems such as the Lower Cretaceous Yageliemu Formation of the Yakela gas condensate field, Kuqa Depression, Tarim Basin, China. This study integrates traditional crossplot techniques with machine learning-based lithology classification and petrophysical evaluation to characterize reservoir heterogeneity. Mineralogical analysis using M-N crossplots identifies a dominantly quartz-rich clastic matrix with subordinate shale content, and this result establishes the compositional basis for lithological interpretation. Lithological classification uses mineralogical constraints and applies diagnostic crossplots and K-means clustering to define four facies, which include clean sandstone, clayey sandstone, shaly sandstone, and shale. This classification provides a framework for evaluating petrophysical variations across reservoir facies. Petrophysical analysis shows significant differences in reservoir quality among facies. Clean sandstone (HFU-01) exhibits the highest reservoir quality with effective porosity of 9.9%, permeability of 10 mD, and favorable flow parameters (RQI = 0.20, FZI = 1.47), which indicate strong flow capacity. In contrast, shale (HFU-04) displays poor reservoir quality with porosity of 1.6%, permeability of 0.002 mD, and low flow potential (RQI = 0.008, FZI = 0.433). Clayey and shaly sandstones show intermediate properties that reflect transitional flow behavior. Gas-bearing intervals across four wells show effective porosity of 4.6–9.8%, permeability of 3.0-3.6 mD, shale volume of 19–24%, and gas saturation of 48–60%, which indicate moderate reservoir heterogeneity controlled by facies distribution. This integrated workflow links mineralogical, lithological, and petrophysical analyses and provides a framework for lithology classification and flow unit characterization. The study improves understanding of reservoir heterogeneity and enhances the reliability of flow unit delineation in clastic reservoir systems.