Estimation of Permeability by Integrating Nuclear Magnetic Resonance (NMR) Logs with Mercury Injection Capillary Pressure (MICP) Data in Tight Gas Sands

It has been a great challenge to determine permeability in tight gas sands due to the generally poor correlation between porosity and permeability. The Schlumberger Doll Research (SDR) and Timur–Coates permeability models, which have been derived for use with nuclear magnetic resonance (NMR) data, also lose their roles. In this study, based on the analysis of the mercury injection experiment data for 20 core plugs, which were drilled from tight gas sands in the Xujiahe Formation of central Sichuan basin, Southwest China, two empirical correlations between the pore structure index ($$ \sqrt {{K \mathord{\left/ {\vphantom {K \varphi }} \right. \kern-\nulldelimiterspace} \varphi }} $$, defined by the square root of the ratio of rock permeability and porosity) and the R35 (the pore throat radius corresponding to 35.0 % of mercury injection saturation), the pore structure index and the Swanson parameter have been developed. To consecutively estimate permeability in field applications, based on the study of experimental NMR measurements for 36 core samples, two effective statistical models, which can be used to derive the Swanson parameter and R35 from the NMR T2 logarithmic mean value, have been established. These procedures carried out on the experimental data set can be extended to reservoir conditions to estimate consecutive formation permeability along the intervals with which NMR logs were acquired. The processing results of several field examples using the proposed technique show that the classification scale models are effective only in tight gas reservoirs, whereas the SDR and Timur–Coates models are inapplicable. The R35-based model is of significance in thin sands with high porosity and high permeability, but the predicted permeability curves in tight gas sands are slightly lower. In tight gas and thin sands, the Swanson parameter model is all credible.