A new correction method for mercury injection capillary pressure (MICP) to characterize the pore structure of shale

Abstract

The economic and accurate measurement of micron or submicron size pores in shale is still a challenge. The mercury injection capillary pressure (MICP) measurement of shale particles is an effective method to evaluate the macropore structure, but it is questionable without proper data interpretation. In this study, the fractal theory was applied to identify the four stages of MICP measurement: Stage A is controlled by the interparticle voids in the particle assemblage; Stage B can reflect mercury intrusion into the pores of the shale; and Stage C and D indicate the shale matrix compression and pore structure change induced by high pressure, respectively. Based on the consistent fractal dimension derived from Stage A, the conformance volume induced by interparticle voids at each pressure step in Stage B was quantitatively simulated using the linear fitting method. The correction results show that simultaneous mercury filling of interparticle voids has an important impact on the pore volume in the pore size range larger than 1 μm. To cover the complete pore size range, the MICP data in Stage C and D were substituted by gas adsorption data.The new correction method for MICP data was verified by the good match between the MICP-gas adsorption combined porosity and the helium porosity of the same sample and by the good agreement between the pore size distribution (PSD) curves from the MICP and N2 adsorption measurements in their overlapped pore size range. Therefore, the MICP and gas adsorption methods can be used in conjunction to characterize the pore structure of shale in the complete pore size range. The application of this method is particularly good for the silty samples of lacustrine shale in the study area compared with the clayey samples. To further improve the convergence of different fluid injection methods, more efforts are needed to determine the optimum particle size class and optimum data interpretation model for different shale lithofacies.