Pore characterization was achieved based on the improved U-net deep learning network model and scanning electron microscope images

Abstract

The SEM image method is commonly used in the qualitative characterization of shale pores. The development of shale micro-reservoir pores can be visually observed through SEM images, but the efficiency of manual image processing is low and subjective. The introduction of deep learning greatly improves the efficiency of pore analysis. In this paper, the argon ion polishing SEM image of Longmaxi Formation shale in southern Sichuan is taken as an example. Intelligent identification and quantitative characterization of pores in shale SEM images are realized by Pore-net network model. Pore-net is based on the U-net network model. The way the model reads the data is changed so that the model does not focus on the region of interest. The number of convolutional layers of the model is increased. The Canny edge extraction algorithm is added. It not only reduces the workload of data set production, but also enhances the ability of network model to identify pores. The results show that the deep learning semantic image segmentation method is suitable for pore recognition of shale SEM images. The full convolutional neural network model is used to train the manually labeled shale SEM images, which can realize the intelligent recognition and quantitative characterization of shale SEM images. Compared with FCN and DeepLab V3+ network model, Pore-net performs better. Only 170 data sets are used to train the model. The Pore-net network model still has a good recognition effect on pores, which solves the problem of low accuracy of traditional pore recognition methods. The deviation between the porosity calculated by the Pore-net network model and the experimental data is between 12% and 19%. Compared with the porosity results calculated by the binarization method and other network models, the results calculated by Pore-net are closer to the real values, which proves that the porosity calculated by the Pore-net network model is reliable.