Characterization of reservoir fluids using an EOS based on perturbation from n-alkanes

Characterization of reservoir fluids using a cubic equation of state (EOS) is conducted under unavoidable uncertainties in pressure-temperature-composition (P-T-x) space. The implicit, non-linear relationship between phase behavior predictions and adjustment parameters also makes fluid characterization non-unique and subjective. Although P-T-x space that phase behavior spans is continuous, different characterization methods have been proposed for different types of reservoir fluids. In our previous research, a method was developed for heavy-oil characterization using the Peng-Robinson (PR) EOS with the van der Waals mixing rules without volume shift. Uncertainty issues in heavy-oil characterization were addressed based on the concept of perturbation from n-alkanes (the PnA method). Pseudo components were initially assigned critical parameters that were optimized for n-alkanes in terms of liquid densities and vapor pressures using the PR EOS. The optimized reference values allowed for well-defined directions for perturbation of pseudo components' critical parameters to match available experimental data. The robust regression algorithm required only three perturbation parameters. In this paper, we extend the PnA method to lighter fluids, such as gas condensates, volatile oils, and near-critical fluids. The main novelty of the new PnA method is that it considers proper interrelationship (psi = a/b(2)) between the attraction (a) and covolume (b) parameters of pseudo components. The regression algorithm developed in this research controls the trend of the psi parameter with respect to molecular weight using a fourth adjustment parameter gamma. The psi and gamma parameters become more important for characterizing lighter fluids. For extra heavy oils, the new PnA method naturally reduces to the previous PnA method, where gamma is zero. Case studies using 77 different reservoir fluids demonstrate the universal applicability, reliability, robustness, and efficiency of the new PnA method. The fluids used consist of 34 heavy and black oils, 12 volatile oils, and 31 gas condensates. Six fluids are near critical among them. The PnA method controls phase behavior predictions monotonically with parameter adjustments and systematically in P-T-x space. This is demonstrated by quantitative prediction of condensation/vaporization behavior of gas condensates and light oils and minimum miscibility pressures for various oil displacements. The PnA method requires no change in the thermodynamic model used, i.e., it can be readily implemented in existing software based on the PR EOS with the van der Waals mixing rules. We also explain how volume-shift parameters affect compositional phase behavior predictions when used as regression parameters in fluid characterization. The PnA method uses no volume shift, and properly couples volumetric and compositional phase behaviors. (C) 2013 Elsevier B.V. All rights reserved.