The tight sandstones of the Upper Triassic Xujiahe Formation(T_3x) constitute important gas reservoirs in western Sichuan.The Xujiahe sandstones are characterized by low to very low porosity (av.5.22%and 3.62%) for the T_3x~4 and T_3x~2 sandstones,respectively),extremely low permeability(av. 0.060 mD and 0.058 mD for the T_3x~4 and T_3x~2 sandstones,respectively),strong heterogeneity,micronano pore throat,and poor pore throat sorting.As a result of complex pore structure and the occurrence of fractures,weak correlations exist between petrophysical properties and pore throat size,demonstrating that porosity or pore throat size alone does not serve as a good permeability predictor.Much improved correlations can be obtained between permeability and porosity when pore throat radii are incorporated. Correlations between porosity,permeability,and pore throat radii corresponding to different saturations of mercury were established,showing that the pore throat radius at 20%mercury saturation(R_(20)) is the best permeability predictor.Multivariate regression analysis and artificial neural network(ANN) methods were used to establish permeability prediction models and the unique characteristics of neural networks enable them to be more successful in predicting permeability than the multivariate regression model.In addition, four petrophysical rock types can be identified based on the distributions of R_(20),each exhibiting distinct petrophysical properties and corresponding to different flow units. The tight sandstones of the Upper Triassic Xujiahe Formation (T_3x) constitute important gas reservoirs in western Sichuan. The Xujiahe sandstones are characterized by low to very low porosity (av. 5.22% and 3.62%) for the T_3x ~ 4 and T_3x ~ 2 sandstones , respectively), extremely low permeability (av. 0.060 mD and 0.058 mD for the T_3x ~ 4 and T_3x ~ 2 sandstones, respectively), strong heterogeneity, micronano pore throat, and poor pore throat sorting. As a result of complex pore structure and the occurrence of fractures, weak correlations exist between petrophysical properties and pore throat size, demonstrating that porosity or pore throat size alone does not serve as a good permeability predictor. Much improved correlations can be obtained between permeability and porosity when pore throat radii are incorporated. Correlations between porosity, permeability, and pore throat radii corresponding to different saturations of mercury were established, showing that the pore throat radius at 20% mercury saturation (R (20)) is the best permeability predictor. Multivariate regression analysis and artificial neural network (ANN) methods were used to establish permeability prediction models and the unique characteristics of neural networks enable them to be more successful in predicting permeability than the multivariate regression model .In addition, four petrophysical rock types can be identified based on the distributions of R_ (20), each exhibiting distinct petrophysical properties and corresponding to different flow units.