Low resistivity contrast oil reservoirs are subtle reservoirs that have no obvious difference in physical and electrical properties from water layers. It is difficult to identify based on the characteristics of the geophysical well logging response. Especially in tight sandstone reservoirs with low porosity and low permeability, the log interpretation effect of low resistivity contrast oil layers is worse. In recent years, data mining technology has been increasingly applied in oil exploration and development, especially for some complex reservoirs with unclear logging response characteristics, and how to use data mining technology to effectively solve some complex problems is of great significance in oilfields. Therefore, support vector machine (SVM) technology was applied to interpret the low resistivity contrast oil layer in this paper. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, the two models were applied to the logging interpretation of the Chang 8 tight sandstone reservoir of the Yanchang Formation in the Huanxian area, Ordos Basin. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method and BP neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the low resistivity contrast oil layer by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.