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Pore Structure Characterization and Fractal Analysis of Lacustrine Shales: Integrating N2 Adsorption, Mercury Intrusion, and Deep Learning-Assisted FIB-SEM 3D Pore Surface Point Cloud Reconstruction

文献类型：期刊论文

作者Li, Guanlin1; Xin, Bixiao2,3; Li, Zongmin1
刊名FRACTAL AND FRACTIONAL
出版日期2026-03-11
卷号10期号:3页码:39
关键词lacustrine shale pore system fractal dimension deep learning point cloud image segmentation
DOI10.3390/fractalfract10030179
通讯作者Xin, Bixiao(xinbx@qdio.ac.cn) ; Li, Zongmin(b18010061@s.upc.edu.cn)
英文摘要Lacustrine shales are key targets for shale oil exploration, yet the quantitative characterization of their complex and heterogeneous pore systems remains a significant challenge, constraining effective reservoir evaluation and development. This study investigates lacustrine shales from the Second Member of the Kongdian Formation by integrating N2 adsorption, mercury intrusion porosimetry, and focused ion beam scanning electron microscopy with fractal analysis. A Mamba-based deep learning model was applied to improve two-dimensional (2D) pore segmentation, and three-dimensional (3D) pore surface point clouds were reconstructed for 3D surface fractal characterization to reduce artifacts associated with conventional 3D reconstruction. The results indicate that the pore system is dominated by inorganic pores, mainly irregular interparticle pores and dissolution pores, while organic pores are scarce. Pore sizes are predominantly concentrated in the range of 5 to 200 nm. Adsorption-derived fractal dimensions exhibit systematic lithofacies differences, with D1 and D2 averaging around 2.47 and 2.56, respectively. These trends are consistent with the 3D pore surface fractal dimension derived from pore surface point clouds (mean 2.48), which supplements the bulk statistical results with direct geometric quantification of surface roughness. The heterogeneity of the pore system is influenced by the coupled effects of mineral composition, organic matter content, and diagenesis. Specifically, the enrichment of clay minerals and dolomite increases the irregularity of pore morphology and results in higher fractal dimensions. In contrast, samples enriched in feldspars and calcite are supported by a rigid granular framework that corresponds to lower 3D surface complexity. Ultimately, these fractal dimensions effectively quantify pore network complexity and reservoir heterogeneity in the Kong 2 shales, offering quantitative support for reservoir characterization and lacustrine shale oil exploration.
WOS关键词KONGDIAN FORMATION ; ORGANIC-MATTER ; CANGDONG SAG ; GAS SHALE ; OIL ; EXPLORATION ; BASIN ; AREA ; DIMENSIONS ; PYROLYSIS
资助项目National Key RD Program[2019YFF0301800] ; National Natural Science Foundation of China[42488101] ; National Natural Science Foundation of China[61379106]
WOS研究方向Mathematics
语种英语
WOS记录号WOS:001726218100001
出版者MDPI
源URL[http://ir.qdio.ac.cn/handle/337002/205036]  
专题海洋研究所_深海极端环境与生命过程研究中心
通讯作者Xin, Bixiao; Li, Zongmin
作者单位1.China Univ Petr East China, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China
2.China Univ Petr East China, Sch Geosci, Qingdao 266580, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Ctr Deep Sea Res, Qingdao 266071, Peoples R China
推荐引用方式
GB/T 7714		 Li, Guanlin,Xin, Bixiao,Li, Zongmin. Pore Structure Characterization and Fractal Analysis of Lacustrine Shales: Integrating N2 Adsorption, Mercury Intrusion, and Deep Learning-Assisted FIB-SEM 3D Pore Surface Point Cloud Reconstruction[J]. FRACTAL AND FRACTIONAL,2026,10(3):39.
APA Li, Guanlin,Xin, Bixiao,&Li, Zongmin.(2026).Pore Structure Characterization and Fractal Analysis of Lacustrine Shales: Integrating N2 Adsorption, Mercury Intrusion, and Deep Learning-Assisted FIB-SEM 3D Pore Surface Point Cloud Reconstruction.FRACTAL AND FRACTIONAL,10(3),39.
MLA Li, Guanlin,et al."Pore Structure Characterization and Fractal Analysis of Lacustrine Shales: Integrating N2 Adsorption, Mercury Intrusion, and Deep Learning-Assisted FIB-SEM 3D Pore Surface Point Cloud Reconstruction".FRACTAL AND FRACTIONAL 10.3(2026):39.

入库方式： OAI收割

来源：海洋研究所

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