A Novel Permeability Prediction Model for Deep Coal via NMR and Fractal Theory

Lei Song, Yongsheng Gu (), Lei Zhang and Xiangyu Wang
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Lei Song: School of Energy and Mining Engineering, China University of Mining and Technology, Beijing 100083, China
Yongsheng Gu: School of Energy and Mining Engineering, China University of Mining and Technology, Beijing 100083, China
Lei Zhang: College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Xiangyu Wang: School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, China

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: A quantitative description of the permeability of deep coal is of great significance for improving coalbed methane recovery and preventing gas disasters. The Schlumberger–Doll research (SDR) model is often used to predict rock permeability, but it has inherent defects in characterizing the pore structure of deep coal. In this study, a permeability model with fractal characteristics (FCP model) is established for deep coal based on nuclear magnetic resonance (NMR). The constants in the SDR model are theoretically explained by the relevant parameters in the FCP model. Centrifugation and NMR experiments were performed to determine the optimal centrifugal force and dual T 2 cutoff values. The results show that the coal samples are mainly composed of micrometer and nanometer pores. The adsorption pores account for the largest proportion, followed by the percolation pores and migration pores. In addition, the prediction accuracy of the FCP model is significantly higher than that of the other three models, which provides a fast and effective method for the evaluation of deep coal permeability.

Keywords: NMR; permeability; fractal dimension; pore structure (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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