Effectively estimating the permeability of shale-gas reservoirs by traditional models is challenging; however, study in this area is lacking and deficient. We have developed a method for predicting the permeability of shale-gas reservoirs from porosity and rock compositions including mineralogy and organic matter content, which is applicable to laboratory data and downhole measurements. First, two sets including 38 samples from the Longmaxi Formations were tested for porosity, permeability, grain density, total-organic-carbon (TOC) content, mineralogical composition, and low-temperature nitrogen adsorption (LTNA). We used Kozeny’s equation to calculate the specific surface area, which was viewed as the effective specific surface in shale formations through comparing with the specific surface from LTNA. Furthermore, the effective specific surface was found to be positively correlated with clays, pyrite, and TOC contents, whereas it was negatively correlated with quartz, feldspar, and carbonates. Then, an empirical equation between the effective specific surface area and rock compositions was established via a partial least-squares method, which can process the serious multicollinearity of various mineral contents. Combined with Kozeny’s equation, this equation enabled a prediction of permeability from porosity and rock composition. The results indicated that the predicted and measured permeability have a reasonable match. Compared with other models, this method avoids the correlations between porosity and minerals, providing better insight to the influence of minerals and organic matter on permeability. The influences of rock composition on permeability are different, and are caused by the different types and sizes of pores developed within the minerals and organic matter. In addition, the new method was successfully applied to the well-log data from a shale-gas well for permeability predictions.