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<p>Image observation method is a key method for shale reservoir evaluation. At the micro scale, scanning electron microscope images can be used to accurately understand the structural characteristics of shale. Most of the current research is to artificially identify the microstructure of shale. This approach has subjective limitations and makes it difficult to process images in batches on a large scale. We take the scanning electron microscope image as the research object, and the shale deep learning theory as the research method to realize the intelligent identification of microscopic substances in the shale scanning electron microscope image. The results show that the improved deep learning model performs better than other deep learning models. The maximum values of Precision, Recall, mAP50 and mAP50-95 reached 0.94442, 0.91695, 0.9579 and 0.71547, respectively. The functions of the optimized Yolov8 model were integrated with SEM technology. In engineering practice, it can assist researchers to quickly locate object substances and obtain high-quality SEM images, effectively improving the efficiency and accuracy of reservoir evaluation. In addition, this technology has great potential for development, and it is expected to play an important role in expanding to a variety of fields such as medicine and materials science by changing the test object.</p>

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Research on intelligent identification of microscopic substances in shale scanning electron microscope images based on deep learning theory

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月6日

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  • Ruigang Zhang,
  • Xiangru Chen,
  • Xin Tang,
  • Qiuqi Chen,
  • Sen Guo,
  • Cheng Liu,
  • Zhengchao Guo,
  • Yuerong Zhou,
  • Xiaoyi Zhou

摘要

Image observation method is a key method for shale reservoir evaluation. At the micro scale, scanning electron microscope images can be used to accurately understand the structural characteristics of shale. Most of the current research is to artificially identify the microstructure of shale. This approach has subjective limitations and makes it difficult to process images in batches on a large scale. We take the scanning electron microscope image as the research object, and the shale deep learning theory as the research method to realize the intelligent identification of microscopic substances in the shale scanning electron microscope image. The results show that the improved deep learning model performs better than other deep learning models. The maximum values of Precision, Recall, mAP50 and mAP50-95 reached 0.94442, 0.91695, 0.9579 and 0.71547, respectively. The functions of the optimized Yolov8 model were integrated with SEM technology. In engineering practice, it can assist researchers to quickly locate object substances and obtain high-quality SEM images, effectively improving the efficiency and accuracy of reservoir evaluation. In addition, this technology has great potential for development, and it is expected to play an important role in expanding to a variety of fields such as medicine and materials science by changing the test object.