Minimum Miscibility Pressure Prediction Method Based On PSO-GBDT Model

Abstract

With the development of EOR technology, CO2 flooding was a very promising method to improve the recovery of conventional and unconventional oil reservoirs. MMP (minimum miscibility pressure ) was one of the important parameters of the CO2 flooding, and the use of an artificial intelligence algorithm can accurately predict the MMP, which was important to evaluate the effect of CO2 flooding development in the reservoir.   This work presents methods to automatically find optimal parameter settings for machine learning model by using an evolutionary algorithm. In this paper, 195 sets of experimental data of MMP were collected and screened from a large amount of literature for model establish, and sensitivity analysis was performed with the Pearson's method for feature selection. Then, five machine learning algorithms were used for regression and comparison. Finally, a particle swarm optimization algorithm was used to optimize the parameters of the machine learning model with best performance. The accuracy of the training set obtained by the hybrid model was 99.9% and the accuracy of the test set was 97.6%. It indicated that the hybrid model are valid and accurate, and it can be used for MMP prediction in both laboratory and actual field.