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Relative permeability is one of the important parameters for reservoir interpretation and productivity prediction. The main methods used to obtain the relative permeability in the laboratory are the steady-state method and unsteady-state method, but both of these are relatively expensive and require long preparation times. Many researchers try to infer the relative permeability curve from other petrophysical parameters. The most representative one is the method used to calculate the relative permeability of the wetting and non-wetting phases according to the resistivity index I. In previous studies, the existing I-Kr models, such as the Li model and Ma model, have been generally effective in sandstone reservoir, yet not as effective in shaly sandstone reservoirs. In this study, in order to solve the problem of low productivity prediction accuracy caused by the complex oil-water relationship in the shaly sandstone reservoirs in the Xujiaweizi area of Daqing, it is necessary to establish a suitable I-Kr model for the shaly sandstone reservoirs in this area. Based on the improved W-S model, the resistivity index I* and its calculation equation are established to eliminate the influence of the additional conductivity of shale. This resistivity index I* is able to more accurately reflect the conductivity of the shaly sandstone reservoirs; thus a new I*-Kr* model of shaly sandstone reservoirs is successfully established by introducing the modification term of wetting phase relative permeability. In addition, for the shaly sandstone reservoirs, a working process is also proposed, which could be applied to wells in the study area. In order to verify the new model, the productivity prediction and reservoir evaluation were carried out for two sections of a well in this area. The comparison between the productivity calculation results and the actual oil test results proves the accuracy and applicability of the I*-Kr* model in this paper, which effectively improved the accuracy of shaly sandstone reservoir interpretation and productivity prediction. [ABSTRACT FROM AUTHOR]