文献类型：期刊文献

英文题名：Fractal Analysis of the Pore Structure of Marine Shale in the Sichuan Basin

作者：Hu, Ke[1];Wang, Zheng[1,2];Wang, Fei[3,4];Pang, Yu[5];Lin, Lixing[1];Chen, Zhuo[7];Shi, Jialin[6]

机构：[1]Univ Alberta, Civil & Environm Engn Dept, Edmonton, AB T6G 1H9, Canada;[2]China Univ Petr, Coll Petr Engn, Beijing 10224, Peoples R China;[3]Northeastern Univ, Coll Resources & Civil Engn, Key Lab, Minist Educ Safe Min Deep Met Mines, Shenyang 110819, Peoples R China;[4]Northeastern Univ, Key Lab Liaoning Prov Deep Engn & Intelligent Tech, Shenyang 110819, Peoples R China;[5]Chengdu Univ Technol, Coll Energy Resources, Chengdu 610059, Peoples R China;[6]East China Univ Sci & Technol, Sch Chem & Mol Engn, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[7]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

年份：2025

卷号：39

期号：30

起止页码：14572

外文期刊名：ENERGY & FUELS

收录：;EI(收录号：20253318981528);WOS:【SCI-EXPANDED(收录号:WOS:001530807200001)】；

基金：This work is supported by the National Key Research and Development Program of China (No. 2019YFC1906700), the National Natural Science Foundation of China (No. 22178097 and No. 52104021) and the Fundamental Research Funds for the Central Universities (No. 2022ZFJH04).

语种：英文

外文关键词：Adsorption - Enhanced recovery - Fractal dimension - Ion beams - Learning algorithms - Learning systems - Machine learning - Petroleum reservoirs - Pore structure - Porosity - Shale

摘要：The fractal characteristics of shale significantly influence pore distribution patterns and surface roughness, thus impacting reservoir properties such as permeability, porosity, and diffusion coefficients. However, existing studies have not fully characterized these fractal features, particularly in ultralow-pressure regions. This study comprehensively investigated the fractal characteristics of shale by integrating low-pressure N2 adsorption, CO2 adsorption, mercury intrusion, focused ion beam-scanning electron microscopy (FIB-SEM), and three-dimensional reconstruction techniques. The Sierpinski fractal model was employed to determine fractal dimensions in the ultralow-pressure interval based on the N2 isotherms and adsorption mechanism. By extracting pores from images obtained using the FIB-SEM technique, the fractal dimensions (D 3) of the three-dimensional digital cores were calculated based on the box-counting model. Additionally, investigating the correlation between porosity and D 3 values revealed a direct logarithmic relationship between porosity and D 3 values. This correlation suggests that the fractal dimensions in shale can extend to other physical parameters associated with porosity. A machine learning algorithm was applied to predict the fractal dimensions of shale from the Sichuan Basin. These findings not only deepen the understanding of shale reservoir properties but also offer a solid theoretical foundation for shale gas development and enhanced recovery.