Evaluation of pore properties in coal through compressibility correction based on mercury intrusion porosimetry: A practical approach

As a commonly used method to evaluate the pore structures of coal, uncorrected MIP data may cause the results to be highly overestimated. In this study, a series of experiments were carried out to characterize the accurate pore structure of coal including MIP, low pressure N2/CO2 adsorption, scanning electron microscopy (SEM) and X-ray computed microtomography (mu CT). Base on the process of mercury penetrate the pore by driven-pressure, it is clear that the mercury intrusion under high pressure can induce the coal matrix compression. Meanwhile, the MIP error (from 36.1% to 130.9% of the pore volume) was derived from coal matrix compression at the high pressure, and the interparticle pore and pockmark effects under low pressure. Here, a new and more practical pore volume correction method was proposed by simplifying the coal matrix compressibility calculation. The new method was tested to be valid for the tested coals with comparison of reported corrected data. The relationship model between intrusion pressure of mercury and pore diameter was revised by the pore morphology. The interparticle pores and pockmark effects mainly occur in the pressure range less than 0.01 MPa. In order to minimize the interparticle pores effect and pockmark effect, a new sample preparation method is recommended for MIP test. This study provides a practical approach to characterize the coal pore structure accurately using the MIP technique and the findings are impactful for the future gas transport modeling in various coals.

Automated summary

This study conducted a series of experiments to characterize the accurate pore structure of coal and proposed a new pore volume correction method for MIP data. The relationship model between mercury intrusion pressure and pore diameter was revised, and a new sample preparation method was recommended to minimize interparticle pores effect and pockmark effect in MIP test. The findings of this study have significant impact on future gas transport modeling in various coals.