Intelligent segmentation and recognition of pores and fractures based on imaging logging

When drilling and developing carbonate rock reservoirs, determining the location and type of the dissolved pores and fractures in the formation is of great significance for judging the leakage channels and storage space in the reservoir. With the help of image recognition technology, recognizing the fractures and dissolved pores in imaging logging images is a difficult point in current research. Recognizing the images requires a large amount of sample data, and the recognition effect is poor in the case of small samples. Therefore, it is proposed to improve sample quality through image segmentation, so as to realize high-accuracy pore and fracture recognition in the case of small samples. The research mainly includes image segmentation and image recognition. Image segmentation is mainly based on threshold segmentation, that is, applying K-means clustering and genetic algorithm to gradually optimize threshold segmentation. Image recognition mainly refers to deep neural network, which recognizes the pore and fracture structures in imaging log images based on the high-quality image after image segmentation. The research results show that the overall recognition accuracy increases from 63.3% to 90.0% before and after image segmentation. In the practical application of the model, the model successfully recognized the high conductivity fractures and the dissolved pores contained in the sample, and improved the image quality through image segmentation, which can realize the high-accuracy recognition of pore structure in small samples.