Abstract:With the proposal of "carbon neutralization goal in 2060", CO2 flooding technology has attracted wide attention again in the field of oilfield development in China. CO2 flooding can not only realize underground storage of carbon resources, but also play a major role in tertiary oil recovery. However, its oil displacement effect is restricted by the miscibility of CO2 and crude oil, so it is necessary to accurately predict the minimum miscibility pressure (MMP) of CO2-crude oil system. Due to the large time cost and error of traditional prediction methods, artificial intelligence algorithm stands out because of its high computational efficiency and accuracy. In this paper, random forest algorithm was used to analyze the main control factors of MMP, and the mole fraction of CO2, H2S, Cj, C2-C5, N2, reservoir temperature, mean critical temperature and other characteristic variables were screened out. Five intelligent algorithms including MLP, GA-RBF, RF, PSO-GBDT and AdaBoost SVR were utilized to establish the MMP prediction model. The final test results show that the PSO-GBDT model has the best MMP prediction effect under the condition of limited data. The mean absolute percentage error (MAPE) of PSO-GBDT is 4.89%, the root mean square error (RMSE) is 0.83, and R2 of the test set is 0.96. This model has the highest accuracy, flexibility and robustness.