Quantitative identification method for pores in shale inorganic components based on pixel information

The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas. However, quantitative identification methods for pores of different inorganic components have not yet been fully developed. For this reason, a quantitative characterization method of inorganic pores using pixel information was proposed in this study. A machine learning algorithm was used to assist the field emission scanning electron microscopy (FE-SEM) image processing of shale to realize the accurate identification and quantitative characterization of inorganic pores on the surface of high-precision images of shale with a small view. Moreover, large-view image splicing technology, combined with quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) image joint characterization technology, was used to accurately analyze the distribution characteristics of inorganic pores under different mineral components. The quantitative methods of pore characteristics of different inorganic components under the pixel information of shale were studied. The results showed that (1) the Waikato Environment for Knowledge Analysis (WEKA) machine learning model can effectively identify and extract shale mineral components and inorganic pore distribution, and the large-view FE-SEM images are representative of samples at the 200 μm × 200 μm view scale, meeting statistical requirements and eliminating the influence of heterogeneity; (2) the pores developed by different mineral components of shale had obvious differences, indicating that the development of inorganic pores is highly correlated with the properties of shale minerals themselves; and (3) the pore-forming ability of different mineral components is calculated by the quantitative method of single component pore-forming coefficient. Chlorite showed the highest pore-forming ability, followed by (in descending order) illite, pyrite, calcite, dolomite, albite, orthoclase, quartz, and apatite. This study contributes to advancing our understanding of inorganic pore characteristics in shale.