Accurate prediction of the viscosity of light crude oils using one-parameter friction theory: Effect of crude oil characterization methods and property correlations

Abstract

The one-parameter friction theory framework using the Peng-Robinson equation of state (PR FT) (Quiñones-Cisneros et al., Fluid Phase Equilibria, 2001) is applied for the viscosity modeling of light, crude oils. Three different methods have been implemented to characterize and determine the composition of these fluids: SARA-based method using the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) EoS (Punnapala and Vargas, Fuel, 2013), SARA-based method using the PR EoS (Abutaqiya et al., Energy & Fuels, 2020), and Single Carbon Number (SCN) method using the PR EoS (Pedersen and Christensen, Taylor & Francis Group, 2007). Both SARA-based methods use the Saturates-Aromatics-Resins-Asphaltenes (SARA) content analysis. Additionally, two different property correlation sets have been used with each characterization method to estimate the critical properties of the generated pseudo-components: Evangelista and Vargas (EV) correlations (Evangelista and Vargas, Fluid Phase Equilibria, 2018) and Pedersen correlations (Pedersen and Christensen, Taylor & Francis Group, 2007). The predictive capabilities of the different modeling schemes are tested against experimental viscosity data for 10 light crude oils from the Middle East after fitting the friction theory parameters to a single viscosity data point at saturation condition. A systematic comparison of the characterization methods revealed that the SARA-based methods with either EoS predict viscosity with higher accuracy (below 5% AAPD) than the SCN method (above 5% AAPD), irrespective of the correlations used. Despite using the relatively simpler PR EoS with SARA-based method, the viscosity predictions are as good as the predictions obtained using the highly advanced PC-SAFT EoS.