Zhao Keying, Zhang Zhanghua
Sichuan Water Conservancy Vocational College, Chengdu, China.
PLoS One. 2025 May 29;20(5):e0323968. doi: 10.1371/journal.pone.0323968. eCollection 2025.
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.
本文通过从扫描电子显微镜（SEM）图像和核磁共振（NMR）实验数据中提取孔隙参数来计算分形维数，全面讨论了平面和空间上的孔隙结构非均质性，并探讨了分形维数与页岩成分及物理参数之间的关系，为页岩储层非均质性研究提供了新思路。对SEM图像的分形维数分析表明，山西组页岩孔隙可分为有机孔隙、粒间孔隙和微裂缝。纳米级孔隙的平均直径为17.13nm至67.65nm，面孔隙率为5.75%至9.37%，微裂缝比例为0.36%至0.72%，平均值为0.53%。ImageJ软件中的ImageJ Weka分割模块智能优化了SEM图像中孔隙的识别程度，以确保准确提取和表征孔隙结构特征。使用Dathe公式对识别出的孔隙计算SEM图像的分形维数：束缚流体孔隙的分形维数（0.4922～0.9396）和可动流体的分形维数（2.9727～2.989），石英含量与束缚流体孔隙的分形维数呈负相关，黏土矿物含量与束缚流体孔隙的分形维数呈正相关，NMR分形维数与有机质含量和成熟度无明显相关性，且NMR分形维数与孔隙度呈负相关，但与渗透率无明显相关性：表明NMR分形维数主要受页岩矿物成分影响；山西组页岩演化程度高但有机质孔隙不发育。储集空间主要由粒间孔隙和微裂缝提供；粒间孔隙和微裂缝非均质性高且连通性差导致渗透率低。本文尝试使用ImageJ Weka分割模块智能优化孔隙识别，提高了孔隙识别的效率和准确性。同时，将SEM图像的分形维数与NMR图像的分形维数相结合来表征储层特征，为定量描述页岩孔隙形态的不规则性提供了依据。
