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Permeability estimates from well logs are essential inputs for reservoir characterization and of great significance to almost all aspects of hydrocarbon development. Borehole image, with high resolution and coverage to highlight subtle heterogeneity of formation, is rarely used for permeability estimation, especially for clastic rocks. Here, we put forward a set of algorithms to automatically extract sand resistivity from microresistivity borehole image to effectively indicate and estimate the intrinsic permeability of porous clastic rocks, and then apply it to the tight sandstone reservoir of Middle Jurassic Shaximiao Formation in Qiulin gas field, central Sichuan Basin. First, equations are deduced, according to the measurement principle and combined with shallow lateral log, to scale borehole image data into resistivity or conductivity depth by depth. Then, based on the volume model and the parallel conductance theory, sand resistivity component R s d is separated from the scaled borehole image by sliding statistics and partitioning with cutoffs equal to the shale volume properly calculated from conventional logs. Subsequently, exponential functions, derived from significant negative correlations between R s d and core permeability K _ c o r e in double logarithmic coordinate, are used to estimate formation permeability. With their correlation coefficients between 0.617 and 0.861, the resultant K_est curves coincide better with subtle variations of K _ c o r e than those estimations derived from conventional logs. This study may also be helpful to the improvement of high-resolution micromodeling and water saturation quantification in shaly sandstones. • Permeability of shaly sandstones is better estimated using borehole image. • Equations to scale borehole image resistivity depth by depth are deduced. • Resistivity components partitioning algorithm of borehole image is provided. • Sand resistivity component is a good negative indicator for permeability variation. • Half coring interval as window length of image logs ensures optimum scale matching. [ABSTRACT FROM AUTHOR]