| Citation: |
WANG Anmin，GAO Yuchao，ZOU Junchao，et al. Quantitative characterization of pore structure in coal measure shales based on deep learning[J]. Coal Science and Technology，2023，51（S2）：183−190. DOI: 10.13199/j.cnki.cst.2022-1597
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Reservoir pore structure is an important factor affecting shale gas exploration and development. In order to accurately characterize the nanopore structure in coal-measure shale reservoirs, this paper takes coal-measure shale in the Muli area of Qinghai province as the research object, and uses shale pore structure images collected from scanning electron microscopy (SEM) to establish a shale pore image data set. A semantic image segmentation model named HAFCN (Hypercolumns Attention Fully Convolutional Networks) was proposed for shale pore segmentation based on deep learning technology. Compared with other three classical semantic segmentation models (FCN, U-Net++, OCRNet models) for pore images recognition, the HAFCN` model had better pore recognition results than other models, with an average intersection-over-union ratio (mIoU) of
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