(591h) Gsaft: Application of the SAFT-γ Mie Group Contribution EoS in the Oil/Gas Industry - From Academic Research to Industrial Deployment

SAFT-γ Mie is a new equation of state recently developed by the Molecular Systems Engineering group at Imperial College London. It is an advanced group-contribution form of the SAFT equation of state making use of the Mie potential for a more accurate and flexible description of the dispersive/repulsive interactions between segments. One of its key characteristics is the accurate description of vapour/liquid phase equilibria, including the region of the critical point, as well as the second-derivative thermodynamic properties such as the thermal expansivity, isothermal compressibility, heat capacity, Joule-Thomson coefficient, and speed of sound.

In 2009, Process Systems Enterprise (PSE) acquired the exclusive intellectual property rights associated with SAFT-γ Mie and related work, for the purpose of incorporating these developments within its gSAFT advanced thermodynamics technology for process modelling.  In late 2010, TOTAL, PSE and Imperial College embarked on a joint project aimed at exploring in detail the applicability, benefits and limitations of this technology on a wide range of mixtures of interest to the oil & gas industry. The current phase of the project is primarily focused on mixtures of hydrocarbons (alkanes and aromatics), carbon dioxide, water and methanol. The main output is a single, consistent set of group parameters capable of accurately describing the behaviour of these generic mixtures within the SAFT-γ Mie framework.

Starting with a brief overview of the SAFT-γ Mie equation of state, this paper primarily focuses on the systematic methodology employed in developing the corresponding like and unlike group parameters. This comprises a sequence of steps including the choice of representative components and mixtures, the definition of an appropriate set of groups required to describe them, the collection of the necessary experimental data, a streamlined set of software tools and workflows employed for the accurate, efficient and error-free estimation of the relevant parameters, and ultimately the deployment of the gSAFT software within process modelling tools. Results illustrating the predictive accuracy of gSAFT for the above mixtures are presented, including representative examples of the simultaneous description of vapour-liquid and liquid-liquid equilibria, the densities of the coexisting phases, and excess properties of mixing.