Continuous Pore Size Distribution Research Articles

Multicomponent diffusion and convection in capillary structures

AbstractA previously developed method (Sotirchos and Burganos, 1988) for studying isobaric multicomponent diffusion in a class of capillary networks is extended for application to the problem of convection and diffusion of gaseous mixtures in porous media. The method is used to develop flux models for nonisobaric transport in capillary structures, which are then compared with corresponding “three‐parameter” dustygas models. Results for discrete and continuous pore size distributions show that the predictions of the developed flux models depend strongly, both qualitatively, and quantitatively, on the procedure used to average the dusty‐gas model equations for a single pore over the pore‐size distribution.

A NMR technique for the analysis of pore structure: Determination of continuous pore size distributions

NMR measurements of the spin—lattice relaxation decay time for a fluid in a porous solid have been previously demonstrated as a significant tool for pore size/structure analysis. The analysis requires the solution of a Fredholm integral equation of the first kind to extract the desired T 1, and therefore, pore size distribution from the relaxation measurements. Earlier work successfully used a nonnegative least-squares (NNLS) technique to solve this problem. However, for stability to be achieved with that approach, the problem must be significantly overconstrained (i.e., more relaxation measurements than pore size distribution points) which limits resolution. In addition, broad distributions are represented as several narrow peaks. In this work, the method of regularization is applied to extracting continuous pore volume distributions from relaxation measurements. The validity of the approach used to calculate the optimum value of the regularization smoothing parameter given an expected noise level and the data is demonstrated using “data sets” generated from known T 1 distributions. Varying levels of random noise are added to these data sets and the T 1 distribution is calculated for comparison to the starting distribution. For error levels in the range of 0.001 to 0.01, agreement between the starting and the calculated distributions is good. The method has also been applied to relaxation data obtained for water in several controlled pore glasses and “random packing of sphere” solids. Agreement to NMR/regularization and mercury porosimetry-derived pore size distributions was excellent with the only deviations being the result of porosimetry measuring pore neck size and NMR measuring the true pore volume to surface area ratio.