Abstract
In this paper, the fractal dimension is calculated by extracting pore parameters from SEM images and NMR experimental data, the pore structure heterogeneity in plane and space is comprehensively discussed, and the relationship between the fractal dimension and shale composition and physical parameters is discussed, providing new ideas for the study of shale reservoirs heterogeneity. Fractal dimension analysis of SEM images reveals that the shale pores of the Shanxi Formation can be divided into organic pores, inter-granular pores and micro-fractures. The average diameter of nano-scale pores is 17.13 nm to 67.65 nm, the surface porosity is 5.75% to 9.37%, and the proportion of micro-fractures is 0.36% to 0.72%, with an average value of 0.53%. The ImageJ Weka Segmentation module in ImageJ software intelligently optimizes the degree of pore recognition in SEM images to ensure accurate extraction and characterization of pore structure features. The fractal dimension of the SEM image was calculated using the Dathe formula for the identified pores: Fractal dimension of bound fluid pore (0.4922 ~ 0.9396) and fractal dimension of movable fluid (2.9727 ~ 2.989), quartz content has a negative correlation with the fractal dimension of bound fluid pores, clay mineral content has a positive correlation with the fractal dimension of bound fluid pores, NMR fractal dimension has no obvious correlation with organic matter content and maturity, and NMR fractal dimension has a negative correlation with porosity, but has no obvious correlation with permeability: indicating that NMR fractal dimension is mainly affected by the composition of shale minerals; The Shanxi Formation shale has a high degree of evolution but the organic matter pores are not developed. The reservoirs space is mainly provided by inter-granular pores and micro-fractures; the inter-granular pores and micro-fractures have high heterogeneity and poor connectivity leads to low permeability.This paper attempts to use the ImageJ Weka Segmentation module to intelligently optimize the identification of pores, which improves the efficiency and accuracy of pore identification. At the same time, it combines the fractal dimension of SEM images and the fractal dimension of NMR images to characterize reservoir characteristics, which provides a basis for quantitatively describing the irregularity of shale pore morphology.