Image segmentation techniques for processing scanning electron microscopy (SEM) images can enhance the efficiency of oil and gas field exploration. This study initially utilizes shale SEM images from the Longmaxi Formation in the Sichuan Basin to review traditional SEM image segmentation methods, including threshold-based, boundary-based and region-based segmentation methods, as well as their limitations. Subsequently, fundamental principles of deep learning techniques in image segmentation are discussed, with a particular focus on the superiority of convolutional neural network (CNN) architectures such as Fully Convolutional Network (FCN), U-Net, and Mask-RCNN in SEM image segmentation research. Finally, the challenges currently faced by the research are analyzed, including difficulties in data annotation, the enhancement of model generalization capabilities, and the processing of multimodal SEM images. The development of automated annotation tools, improvement of model generalization ability through transfer learning and multi-task learning, and multimodal fusion techniques are future research directions. This study offers a reference and evaluation for SEM image recognition and analysis based on deep learning.