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<p>Permeability prediction and sweet-spot identification in tight sandstone reservoirs are challenging because of complex pore–throat structures and strong heterogeneity. This study uses data from 12 cored wells in the Chang 8 Member, Jiyuan Oilfield (western Ordos Basin) to develop a permeability-driven integrated workflow for reservoir evaluation. We first build an SE-ResNet18 model with one-dimensional convolution and residual learning to capture vertical continuity in well logs, achieving R² = 0.86 and RMSE = 0.287 mD for permeability regression. We then design a well-log-based sweet-spot index (Issp) and embed it as a geological prior through an attention-gating mechanism to form a knowledge-guided model (KG-SE-ResNet18). This knowledge guidance improves reservoir-type classification accuracy from 86.95% to 89.28%. Overall, the proposed framework enhances both prediction accuracy and geological consistency, providing a practical approach for fine-scale reservoir evaluation and well-placement optimization in tight sandstones.</p>

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An integrated method for permeability prediction and fluid identification in tight sandstone reservoirs using geological-prior-guided attention networks: a case study of the X Block, Chang 8 member, Jiyuan oilfield

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月6日

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  • Xinyu Li,
  • Yuming Liu,
  • Bingbing Zhang,
  • Jingjing Luo,
  • Hengzhi Liu,
  • Qi Chen

摘要

Permeability prediction and sweet-spot identification in tight sandstone reservoirs are challenging because of complex pore–throat structures and strong heterogeneity. This study uses data from 12 cored wells in the Chang 8 Member, Jiyuan Oilfield (western Ordos Basin) to develop a permeability-driven integrated workflow for reservoir evaluation. We first build an SE-ResNet18 model with one-dimensional convolution and residual learning to capture vertical continuity in well logs, achieving R² = 0.86 and RMSE = 0.287 mD for permeability regression. We then design a well-log-based sweet-spot index (Issp) and embed it as a geological prior through an attention-gating mechanism to form a knowledge-guided model (KG-SE-ResNet18). This knowledge guidance improves reservoir-type classification accuracy from 86.95% to 89.28%. Overall, the proposed framework enhances both prediction accuracy and geological consistency, providing a practical approach for fine-scale reservoir evaluation and well-placement optimization in tight sandstones.