Use of neural networks for prediction of vapor-liquid equilibrium k-values for light hydrocarbon mixtures

Equilibrium ratios play a fundamental role in the understanding of phase behavior of hydrocarbon mixtures. They are important in predicting compositional changes under varying temperatures and pressures conditions in reservoirs, surface separators, production and transportation facilities. In particular they are critical for reliable and successful compositional reservoir simulation. This paper presents a new approach for predicting K-values using Neural Networks (NN). The method is applied to binary and multicomponent mixtures, K-values prediction accuracy is in the order of the tradition methods. However, computing speed is significantly faster.

Use of neural networks for prediction of vapor-liquid equilibrium k-values for light hydrocarbon mixtures Conference Paper