From BO (Binary) to Ternary Fluid Representation for Simulation of Condensate Recovery by Lean Gas Injection
Authors: Barroux Claire 
Published: December 2007

Abstract
For the simulation of condensate recovery by lean gas injection below the gas dew point, a common statement is that most of the Black-Oil approaches are not reliable, and that typically 6 to 8 pseudo components in a compositional simulation model are needed, the draw back being a much more considerable computation time.
A novel approach, inspired in some extent from BO modeling, has been found adequate to simulate the vaporization effects in the reservoir as lean gas is injected below the dew point. This approach uses a three-component representation of the reservoir fluid. A special attention has been devoted to establish a methodology of practical use for generating the PVT data. To recover the detailed compositional information lost by the use of pseudo components for the dynamic computations, a methodology for delumping the results of the simulation has also been considered.
The paper presents firstly the bases of the lumping procedure used for building the ternary representation, the methodology for generating the PVT data, and considerations for delumping the simulation results. Then, test examples are provided, with a comparison between the simulation results obtained with the ternary representation, and with the reference (detailed) compositional representation. Results are shown to be in good agreement, while considerable CPU reduction factors are obtained.

Introduction
Driven by the ever increasing power of computation machines, the trend in reservoir simulation is:the handling of more and more complex, larger and larger reservoir simulation models1,solving in a coupled way the surface network and reservoir simulations, this coupling being recognized as a critical technology for field development/optimization studies particularly in the offshore area.
Given the very large scale of the global problems to be solved, considerable efforts in reservoir simulation technology are then devoted to find techniques for reducing the computation times (CPU).
Computation time increasing approximately as the square of the number of components, CPU savings can be obtained using, in the reservoir simulation model, a fluid representation based on a minimal number of components.
When useable, a hydrocarbon fluid two-component representation allows for a minimization of the computation time. But the practical range of application of binary fluid formulations is limited, the main intrinsic limitation of these formulations coming from the application of the Gibbs' phase rule, as detailed for example by Goldthorpe2 : in the two-phase region, at any given temperature and pressure, the compositions of equilibrium liquid and vapor phases are fixed.
In particular, the binary fluid models can give erroneous results when used for modeling vaporization effects occurring in the reservoir when a lean gas is injected to extract heavier components from the condensate (or oil) phase3.
The various BO formulations4,5,6,7,8 are basically two component representations: an heavy component ‘H’ and a and a volatile one ‘V’, originally presented as corresponding to the ‘stock tank oil’ and to the ‘surface gas’, main enhancements from the original BO formulation consisting in:enabling the presence of the heavy component in the gas phase ;and dissociating the reservoir and surface PVT representations (fluid properties and equilibrium coefficients).
However, these enhancements have not acted on the binary nature of the BO representations, the formulations remaining then of questionable use for simulating non equilibrium gas injection processes when significant mass transfers are expected to occur between the hydrocarbon phases in the reservoir.
The only way to overcome this fundamental limitation is, in one way or another, to introduce a third component.
Cook et al.9 have introduced a compositional index representing the cumulative volume of injection dry gas per volume of oil, the corresponding input data being generated using a linear cell-to-cell model. Discussion with Henry10 has highlighted the limitations of Cook's approach.