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Data from: Fractal characteristics of shale pore structure and its influence on seepage flow

Citation

Wang, Shengwei (2021), Data from: Fractal characteristics of shale pore structure and its influence on seepage flow, Dryad, Dataset, https://doi.org/10.5061/dryad.05qfttf2r

Abstract

The migration law of shale gas has a significant influence on the seepage characteristics of shale, and the flow of the gas is closely related to the pore structure. To explore the influence of shale pore parameters on permeability in different diffusion zones, the pore structure of the shale in the Niutitang Formation in Guizhou, China, was analyzed based on liquid nitrogen adsorption experiments and nuclear magnetic resonance experiments. The relationship among fractal dimension, organic carbon content (TOC), and BET specific surface area was analyzed based on the fractal dimension of shale pores calculated using the Frenkel‒Halsey‒Hill model. Shale permeability was calculated using the Knudsen number and permeability equation, and the influence of the fractal dimension and porosity in different diffusion zones on shale permeability was analyzed. Previous studies have shown that: (1) the pores of shale in the Niutitang Formation, Guizhou are mainly distributed within 1‒100 nm, with a small total pore volume per unit mass, average pore diameter, large BET specific surface area, and porosity; (2) fractal dimension has a negative correlation with average pore diameter and TOC content and a quadratic relationship with BET specific surface area; (3) permeability has a positive correlation with Kn, porosity, and fractal dimension. In the transitional diffusion zone, fractal dimension and porosity have a significant impact on permeability. In the Knudsen diffusion zone, porosity has no obvious effect on permeability. The methodologies and results presented will enable more accurate characterization of the complexity of pore structures of porous media, and allow further understanding of the seepage law of shale gas.

Usage Notes

The following data set is a complete list of experiments.

Funding

National Natural Science Foundation of China, Award: 51874107

Major Applied Basic Research Project of Guizhou Province, Award: JZ2014-2005

The Science and Technology Funding Projects of Guizhou Province, Award: 2018-5781