Determination of Interaction Coefficients and Critical Properties of Heavy Fractions for Equation of State Computations: An Automated Algorithmic Approach

Abstract

ABSTRACT Equations of state are widely used in compositional simulation to predict phase distribution and composition, which in turn facilitates prediction of compositionally dependent properties such as density, viscosity, interfacial tension, and enthalpy. A well-known problem confronted with naturally occuring hydrocarbon systems is characterization of the high molecular weight fraction (usually C7 +). The high molecular weight fraction must be split into an optimum number of pseudo-components each having a prescribed set of critical properties and interaction coefficients. Since procedures for doing this are largely empirical, typical equation of state packages must be tuned to available experimental data. Often available experimental data are limited in scope, or the ultimate simulation involves significant changes in temperature, pressure or composition. In each situation, tuned pseudo-component critical properties and/ or interaction parameters will manifest important temperature, pressure, and composition dependencies. Thus, a versatile phase description can effectively be ensured if tuning is automated internally within the equation of state package. In this paper, a methodology for achieving this task is described. First, the heavy fraction is split into a defined number of pseudo-components and starting values of binary interaction parameters and critical properties are obtained by using readily available correlations in the literature and/or mixing rules. Then, a linear optimization technique (two-phase method) is adopted for non-linear applications by using iterative techniques (iterative variables being pseudo-component binary interaction coefficients with CH4 and pseudo-component critical properties). Tuned sets of binary interaction parameters and critical properties of pseudo-components for a cubic equation of state are generated by using experimental depletion curves. As the computation progresses the tuned set replaces the default set to achieve further improvements. The success of this linear optimization routine is demonstrated for different reservoir oils.