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<p>Reliable lithology identification in shale reservoirs is challenged by thin interlayers, heterogeneity with depth, and noise in electrical imaging logs. To address these limitations, we propose a deep-learning framework that integrates a modified You Only Look Once v5 object detector (YOLOv5) detector with K-means clustering to enhance feature extraction and recognition. The study employs a curated dataset of 2355 electrical imaging samples covering eight lithofacies, stratified to preserve class balance and validated through a combination of layered K-fold cross-validation and depth-aware sliding-window evaluation. Image quality was improved using Mosaic augmentation and Hue-Saturation-Value (HSV) perturbations, and lithological labels were cross-checked with geochemical parameters to ensure robustness. Compared with Convolutional Neural Networks (CNNs), traditional geochemical and fractal methods, and standard YOLOv5, the proposed model achieved the highest overall accuracy (97.1%) and demonstrated significant improvements in recognizing thin-layered (&lt; 5&#xa0;cm) gray mudstone. Statistical tests confirm that the improvements over baselines are highly significant (<i>p</i> &lt; 0.01). Beyond accuracy, the framework provides practical interpretability through localized bounding-box overlays with confidence scores, enabling geologists to visually validate predictions on logs. This work contributes a data-driven and interpretable solution for lithology recognition, advancing the reliability of shale reservoir characterization.</p>

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Geochemical-parameter-guided multimodal lithology identification for shale oil reservoirs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Dahai Wang,
  • Lichi Ma,
  • Tao Zhang,
  • Xunxun Fu,
  • Guojun Wang,
  • Changan Shan

摘要

Reliable lithology identification in shale reservoirs is challenged by thin interlayers, heterogeneity with depth, and noise in electrical imaging logs. To address these limitations, we propose a deep-learning framework that integrates a modified You Only Look Once v5 object detector (YOLOv5) detector with K-means clustering to enhance feature extraction and recognition. The study employs a curated dataset of 2355 electrical imaging samples covering eight lithofacies, stratified to preserve class balance and validated through a combination of layered K-fold cross-validation and depth-aware sliding-window evaluation. Image quality was improved using Mosaic augmentation and Hue-Saturation-Value (HSV) perturbations, and lithological labels were cross-checked with geochemical parameters to ensure robustness. Compared with Convolutional Neural Networks (CNNs), traditional geochemical and fractal methods, and standard YOLOv5, the proposed model achieved the highest overall accuracy (97.1%) and demonstrated significant improvements in recognizing thin-layered (< 5 cm) gray mudstone. Statistical tests confirm that the improvements over baselines are highly significant (p < 0.01). Beyond accuracy, the framework provides practical interpretability through localized bounding-box overlays with confidence scores, enabling geologists to visually validate predictions on logs. This work contributes a data-driven and interpretable solution for lithology recognition, advancing the reliability of shale reservoir characterization.