Abstract: In order to predict the effect of CO2 immiscible flooding rapidly and accurately, a prediction model based on support vector machine was established. It takes 13 geological and engineering parameters (i.e. the ratio of residual formation pressure and CO2 miscibility pressure, porosity, permeability, reservoir mid-depth, net pay, formation temperature, relative density of crude oil, oil saturation, oil viscosity, coefficient of permeability variation, injection-production ratio, injection rate, and the ratio of water/gas alternating injection) as input parameters, and the average daily oil increment per well as output parameter. with six CO2 immiscible flooding projects and 1 CO2 miscible flooding project as training samples, and two CO2 immiscible flooding projects and one CO2 miscible flooding project as testing samples in China, the accuracy of the model was verified. The results showed that average relative error between predicted value and actual value of above 3 samples was 5.57%, which met the engineering requirement. The model was applied to predict the effect of CO2 immiscible flooding in Yaoyingtai Oilfield, indicating a relative error of only 1.30% in relation with the actual value. It suggested that the method based on support vector machine is feasible and effective to predict the effect of CO2 immiscible flooding.