Insights into tuning of equations of state models of natural gas (LNG & CNG) and Liquefied Petroleum Gas (LPG) bearing petroleum reservoir fluids

Gas condensates or retrograde gases are particularly valuable because they are primarily targeted for natural gas, propane and butane production, owing to their compositional characteristics, i.e., predominantly methane, appreciable intermediates and a small fraction of heavy hydrocarbons. Generally, these fluids originally exist in the reservoir in a single vapor phase at relatively high pressure and high temperature conditions. Although, a light condensate is produced on the surface, original fluid composition in the reservoir remains unchanged as long as the reservoir pressure is higher than the dew point pressure. Continued pressure depletion below the dew point causes the condensate (retrograde) to appear also in the reservoir and production tubing, which hampers the overall productivity. These characteristics are primarily attributed to composition, prevalent pressure, temperature and phase behavior. Equations of state (EOS) models are routinely employed to model the phase behavior of gas condensates. However, considering the compositional disparity (high methane and low heavy component mole fraction) and uncertainty in characterizing the heavy components; EOS modeling offers a significant challenge, which is handled by characterizing the heavy components and/or by tuning the models. The performance of the characterized fluid is tested by comparing the predicted values and experimental data such as dew point and constant volume depletion (CVD) liquid drop out. Tuning consists of adjusting input data to an EOS, for e.g., to match the measured dew point; the tuned model is then applied to predict the phase behavior and properties at other conditions. However, tuning is not trivial considering the number of input data available to achieve an adjusted model especially considering the fact that an incorrectly tuned model may have negative consequences. This paper specifically deals with tuning of EOS models and offers practical insights into tuning on the basis of a popularly used Peng-Robinson EOS, for application to gas condensates. The study presented in this paper is based on eleven different gas condensates of diverse overall compositions and extensive experimental data. The results obtained during the course of this study indicate that the most effective and reliable tuning is achieved by characterization of the heavy end and small adjustment in the critical temperature of the extended heavy end plus fraction. We believe that the results presented are of significant importance in the recovery of hydrocarbon components that yield natural gas, which can be converted to LNG or CNG, and LPG for shipping, transportation or direct end use as a fuel.