Modeling of Two-Phase Gas Deviation Factor for Gas-Condensate Reservoir using Artificial Neural Network

In petroleum engineering, reservoir fluid characterization is of great importance. Accurate determination of the two-phase gas deviation factor is essential in modeling gas-condensate and gas reservoirs, pipeline flow and reserve estimation, this is because the reservoir fluid is in a two-phase state at pressures below the dew-point pressure. Correlations are replete for predicting single-phase gas deviation factor using different Equation of State (EOS), but no correlation have been found to accurately predict the two-phase gas deviation factor.