Experimental Investigation and Estimation of Light Hydrocarbons Gas-Liquid Equilibrium Ratio in Gas Condensate Reservoirs through Artificial Neural Networks

Equilibrium ratios for the mixture of different components are very important for many engineering application processes. Different numerical methods were explored and applied to ensure efficient estimation of gas-liquid equilibrium ratio. In this paper, the Artificial Neural Network (ANN) approach along with data of experiments performed on 25 gas condensate reservoirs has been utilized to obtain a relationship of gas-liquid equilibrium ratios in gas condensate reservoirs. The relationship between the gas-liquid equilibrium ratio and parameters of components of a mixture (critical temperature, critical pressure, and acentric factor) has been derived. Finally, the results of ANN have been compared to the proposed correlations in the literature and results of the equation of state. This investigation demonstrated that the result of ANN is more precise than the equation of state and existing empirical correlations. Whereas comparison between experimental data of 3 gas condensate samples by ANN, EOS, and existing empirical correlation show that the average absolute error for ANN was between 7.82 to 13.74% and for others was between 29.99 to 94.99%.