a Ebrahimi - Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
h Rasouli - Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
f Rashidi - Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran
e Khamehchi - Department of Chemical Eng., Amirkabir University of Technology, Hafez Ave., Tehran, Iran

An important factor in the design of gas injection projects is the minimum miscibility pressure (MMP). A new genetic algorithm (GA)-based correlation and two neural network models (one of them is trained by BP algorithm and another is trained by PSO algorithm) have been developed to estimate the CO2–oil MMP. The correlation and models use the following key input parameters: reservoir temperature, molecular weight of C5+, mole percentage of the volatiles and intermediate components (for the first time, the mole percentages are used as independent variables). Then results have been validated against experimental data and are finally compared with commonly used correlations reported in the literature;The results show that the neural network model trained by BP algorithm and the correlation that has been developed by GA can be applied effectively and afford high accuracy and dependability for MMP forecasting

CO2–Oil MMP, Genetic Algorithm, Neural Network, BP, PSO, Intelligent Proxy