Powder Technology, Vol.325, 11-20, 2018
Fractal dimensions of low rank coal subjected to liquid nitrogen freeze-thaw based on nuclear magnetic resonance applied for coalbed methane recovery
The aims of this research are to quantitatively evaluate the complexity of the pore structure in coal frozen with liquid nitrogen (LN2) and then study the influence of the modified pore system on coalbed methane (CBM) extraction. To do this, nuclear magnetic resonance (NMR) and fractal dimension theory were used to determine the properties of the coal's pore system after samples of low rank coal had been frozen and then thawed. The fractal dimensions of pores in frozen-thawed coal samples were divided into five types according to pore size and the state of the fluid in the coal pores. The results showed that the fractal dimension D-A of adsorption pores was less than two, indicating that these pores did not exhibit fractal characteristics. The fractal dimensions D-ir and D-T representing closed pores and total pores presented low fitting precision, so the closed pores showed insignificant fractal characteristics. However, the fractal dimensions D-F and D-s representing open pores and seepage pores had high fitting precision, suggesting that open and gas seepage pores exhibited a favorable fractal characteristic. Correlation analysis revealed that D-F and Ds were negatively correlated with LN2 freezing time and the number of freeze-thaw cycles. After being frozen and thawed, coal porosity and permeability showed a strong negative correlation with fractal dimension and this relationship allowed predictive models for permeability and fractal dimensions (D-F and D-s) to be constructed. The models showed that the smaller the fractal dimension, the more uniformly the pores were distributed and the higher their degree of connection. These properties favor the production of CBM. This study also showed that compared with single LN2 freezing events, repeated cyclic freezing with LN2 followed by thawing is more favorable for CBM production. (C) 2017 Elsevier B.V. All rights reserved.