Quantitative characterization of pore structure in coal measure shales based on deep learning

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 0.8576 and a pixel accuracy of 0.97, so that the purpose of rapid analysis of shale pore SEM images was achieved, and various parameters of pore structure was obtained. Compared with the identified pore parameters with the original pore parameter values (Ground-truth), it is found that the pore structure parameters of the two are similar, which confirms the reliability of the model. The average shape factors of small, medium and large pore diameter sections are 1.65, 2.38, and 4.10, respectively, and their average aspect ratios are 2.97, 2.76, and 3.01, respectively, indicating that with the diameter increase of shale pores, the pore shape is more irregular.