Nuclear magnetic resonance cryoporometryas a tool to measure pore size distribution of shale rock

Shale gas has recently emerged as an energy source that offers the opportunity for a number of countries for national energy independence and security. Shale gas is also less-carbon energy compared to coal and oil which can immediately serve as a blue bridge from carbon-intensive energy to renewables. In order to optimize the shale well productivity, characterization and quantification of shale pore architecture is one of the key parameters to model the gas flow behavior in subsurface shales. Transport properties, adsorption and diffusion, of shale gas are influenced, and sometimes even dominated by the pore structure of shales including the size, shape and connectivity of pore and pore networks. To accurately characterize the pore structure of shale is a challenging task in geoscience community. Various techniques, such as Scanning Electron Microscope (SEM), Mercury Intrusion Porosimetry (MIP) and Nitrogen Adsorption Method (NAM) and others, have been widely used to characterize pore structure of geomaterials. However, the built-in limitations of each technique were commonly reported in terms of applicability and accuracy due to the complexity and heterogeneity of the shale pore structure. Taking NAM and MIP for instance, these two techniques routinely involve material alteration which makes them potentially sensitive to contraction and pore interconnectivity, as well as the pressure-induced damage. The nuclear magnetic resonance cryoporometry, a novel and emerging technique, can probe pore size distributions from nano- to micro-scales based upon the depressed melting point of a confined liquid. Liquid and solid states in the NMR cryoporometry differ greatly in the transverse relaxation time, generally more than three orders of magnitude, which provides a relatively wide range for measuring pore size distribution. The resolution in NMR cryoporometry, are controled by the Gibbs-Thomoson coefficient of the liquid imbibed in a sample as well as the instrumental parameters such as temperature spread in the sample and temperature step size. In this study, two standard molecular sieves with pre-known pore structures were initially used to assess the applicability and the accuracy of this technique. Additionally, in order to evaluate the NMR cryoporometry results, both MIP and NAM were employed to characterize the pore structure on the same sample. We tested on both bulk matrix specimens and pulverized shale samples. The results demonstrate that MIP and NAM were comparable NMR cryoporometry and the pore size distributions were found to be similar across these techniques on both bulk and pulverized samples. The NMR cryoporometry was found to be a promising technique to analyze both total and effective porosities by measuring the same rock with pulverized and bulk sample, respectively. Besides pore size distribution and porosity, NMR cryoporometry has a unique feature to analyze larger and potentially arbitrarily-shaped objects and is applicable to samples in aqueous environment. Although it has not been widely used as a stand-alone technique, NMR cryoporometry has become a favored tool for calibrating NMR relaxometry due to its ability to provide accurate, reproducible, and unambiguous results. NMR cryoporometry is a promising and powerful tool when combined with other techniques including NMR relaxometry, spectroscopy, diffusion, and imaging. To summarize, NMR cryoporometry shows the applicability of pore characterization for porous absorbents and absorbates and it provides a wider spectrum of pore information including pore morphology, connectivity, heterogeneity, fluid-surface interactions, and the behavior of binary liquids. It is a promising and valuable technique for pore structure characterization for gas shales. In addition, it will help us improve the understanding of pore surface interactions and its related phenomena, and to discover the fundamental mechanisms behind the behavior of molecules in the confinement when combining with powerful computer modeling simulations.