A new approach for predicting equilibrium ratios of hydrocarbon heavy fractions: Focus on the effect of mixture composition

• A new practical correlation is presented for predicting K-values of heavy fractions.
• The correlation is developed based on K-value data generated for 12 fluid samples.
• Dependency of K-values on composition and binary interaction parameters is investigated.
• Applicability and accuracy of the new method are examined.
• Sensitivity analysis of new model relative to heavy fraction properties is evaluated.

This article introduces a new correlation for estimating equilibrium ratios (K-values) of heavy fractions which have great effects on accuracy and speed of phase equilibria calculations. The proposed correlation directly considers the effects of mixture composition and binary interaction parameters on K-values in terms of the temperature dependent term in the Peng–Robinson equation of state (PR EOS). To develop the correlation, the PR EOS is tuned for 12 crude oil samples based on laboratory data. Equilibrium ratios for heavy fractions from C11 to C20+ are calculated at different pressures and temperatures for each sample using the tuned PR-EOS. These equilibrium ratios are then correlated with model input parameters through a complete study of trends in K-value data. In order to examine the accuracy of proposed correlation, K-values obtained from the tuned PR EOS for two test samples are predicted using the new method and other popular correlations. The new correlation shows a better agreement with the tuned PR EOS results in comparison with methods of Whitson–Torp, Rashidi et al. and Ghafoori et al. Absolute average relative errors of predictions have been improved for up to 7% and 10% for black oil and volatile oil test sample, respectively. Sensitivity analysis of variations in K-values with respect to changes in heavy fraction properties has also been conducted to compare the behavior of empirical correlations with the PR EOS. Results indicate that the new model represent the most accurate behavior comparing to other investigated methods.