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Fracture characterization and permeability prediction by pore scale variables extracted from X-ray CT images of porous geomaterials

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Science China(Technological Sciences) 2020年第5期63卷 755-767页

作者： ZHAO Zhi ZHOU Xiao-Ping QIAN Qi-Hu School of Civil Engineering Chongqing UniversityChongqing 400045China State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact PLA University of Science and TechnologyNanjing 210014China

Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous ***,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is *** results indicate that the porescale permeability is prominently improved by *** addition,excellent agreements are found between the measured and the estimated pore scale variables and *** established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.

关键词： sandstones X-ray CT images pore scale variables permeability prediction cracks characterization

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Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2024年第1期5卷 282-293页

作者： Zhiping Chen Jia Zhang Daren Zhang Xiaolin Chang Wei Zhou State Key Laboratory of Water Resources Engineering and Management Wuhan UniversityWuhan 430072China Institute of Water Engineering Sciences Wuhan UniversityWuhan 430072China Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering of Ministry of Education Wuhan UniversityWuhan 430072China

Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological ***,the complexity of porous media often limits the effectiveness of individual prediction *** study introduces a novel Particle Swarm Optimization-based permeability Integrated prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction ***,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock *** is observed that these methods exhibit computational biases in certain permeability *** PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their *** PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction *** outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.

关键词： Porous media Particle swarm optimization algorithm Multi-source data integration permeability prediction

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Quantitative two/three-dimensional spatial characterization and fluid transport prediction of macro/micropores in Gaomiaozi bentonite

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1568-1579页

作者： Jiangfeng Liu Shijia Ma Hongyang Ni Hai Pu Xiaozhao Li Shaojie Chen State Key Laboratory for Geomechanics and Deep Underground Engineering and School of Mechanics and Civil EngineeringChina University of Mining and TechnologyXuzhou221116China CAEA Innovation Center on Geological Disposal of High Level Radioactive Waste Beijing Research Institute of Uranium GeologyBeijing100029China Key Laboratory of Mining Disaster Prevention and Control Shandong University of Science and TechnologyQingdao266590China

The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy(SEM) and focused ion beam-SEM(FIB-SEM), the pore structure of bentonite was characterized at different scales. First, a reasonable gray threshold was determined through back analysis, and the image was binarized based on the threshold. In addition, binary images were used to analyze bentonite’s pore structure(porosity and pore size distribution). Furthermore, the effects of different algorithms on the pore structure characterization were evaluated. Then, permeability calculations were performed based on the previous pore structure characteristics and a modified permeability prediction model. For permeability prediction based on the three-dimensional model, the effect of pore tortuosity was also considered. Finally, the accuracy of numerical calculations was verified by conducting macroscopic gas and alcohol permeability experiments. This approach provides a better understanding of the microscale mechanism of gas transport in bentonite and the importance of pore structures at different scales in determining its seepage characteristics.

关键词： Gaomiaozi(GMZ)bentonite Pore structure permeability prediction Focused ion beam-scanning electron microscopy(FIB-SEM)

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