Relative permeability, as a key parameter characterizing multiphase fluid flow behavior in porous media, holds significant importance across various fields, such as reservoir evaluation and engineering geology. However, measuring relative permeability is time-consuming and costly. Considering the analogy between fluid flow and electrical current conduction provides a novel approach for studying multiphase flow characteristics using resistivity data. An integrated oil–water relative permeability and resistivity co-measurement experiment was specifically designed for a complex argillaceous sandstone reservoir in a block, referred to as Block A. Research has shown that as the resistance coefficient increases, the water and oil relative permeability decrease and increase, respectively. As the porosity–permeability comprehensive index increases and the shale content decreases, corresponding to the same resistance coefficient, the water and oil relative permeability show increasing and decreasing trends, respectively. The integration of tortuous capillary tube theory and three-water model concepts, combined with the flow-current similarity principle, has enabled the development of a novel relative permeability–resistivity correlation model that is applicable to complex argillaceous sandstone formations. The application of actual data from the study area shows that the relative errors of the water- and oil-phase relative permeability calculated by the proposed model are both small, at 16% and 8.6%, respectively. The model is validated to better characterize multiphase fluid flow in rocks, offering a new approach for accurately calculating relative permeability based on formation resistivity data.

Oil–water relative permeability is a critical parameter for reservoir fluid property evaluation and productivity prediction, playing a vital role in reservoir identification and development. However, the correlation between relative permeability and resistivity remains unclear for complex conductive mineral-bearing argillaceous sandstone reservoirs. In this study, twenty-eight artificial core samples were prepared and compacted under simulated reservoir temperature and pressure conditions. Simultaneous oil–water relative permeability and resistivity measurements were performed on these samples at two brine salinities (7000 ppm and 3000 ppm). The analysis reveals that under the same resistivity index, both conductive-mineral content and shale content exhibit a negative correlation with water-phase relative permeability but a positive correlation with oil-phase relative permeability. In contrast, porosity–permeability conditions and injected-water salinity show a positive correlation with water-phase relative permeability and a negative correlation with oil-phase relative permeability. Furthermore, the effect of salinity on the relative permeability–resistivity relationship intensifies as porosity–permeability conditions improve. Based on the consistency between movable water seepage and conductive paths, a novel relative permeability–resistivity model was developed for the target reservoirs by integrating seepage and conductive theories, with a generalized Archie’s equation adopted to calibrate the effects of conductive minerals and shale on rock conductivity. The model parameters are optimized using the Newton–Raphson iteration algorithm, and empirical correlations between the undetermined coefficients and reservoir parameters are developed. Validated against two independent cores with the most complex mineral compositions (SH2-3 and SH3-3), the model yields average relative errors of 9.8% and 13.4% for water-phase relative permeability, and 4.4% and 5.4% for oil-phase relative permeability, respectively, demonstrating its high calculation accuracy. It enables efficient prediction of reservoir relative permeability using resistivity data, providing a new theoretical and technical support for low-resistivity oil reservoir identification and productivity assessment.