General Thermodynamic Parameterization for multicomponent multiphase flow
Abstract
We present a general parameterization of the thermodynamic behavior of multiphase, multicomponent systems. The phase behavior in the compositional space is represented using a low dimensional tiesimplex parameterization. For example, these are tielines and tietriangles for two and threephase systems, respectively. This parameterization improves the robustness of the phase behavior representation (e.g., phase identification) as well as the efficiency of various types of compositional computations. We demonstrate this Compositional Space Parameterization (CSP) framework for several multiphase multi component porous media flow problems. Largescale compositional simulation in highly heterogeneous reservoirs, involving a large number of components, is one type of applications. In the standard compositional simulation approach, an Equation of State (EoS) is used to describe the phase behavior. For each gridblock, given the temperature, pressure and overall compositions, the EoS is used to detect the phase state (e.g., one, two, or more phases), and if multiple phases are present, calculate the phase compositions. These EoS computations can dominate the overall simulation cost. We compare our adaptive CSP approach with standard EoS based simulation for several challenging problems of practical interest. The comparisons indicate quite clearly that the CSP strategy is more robust, and that it leads to an order of magnitude gain in computational efficiency. Another type of applications is an equilibrium flash calculation of systems with a high number of phases (e.g. three or more). The complexity and strong nonlinear behaviors associated with such systems pose serious difficulties for standard techniques. Here, we describe an effective tiesimplex parameterization for such systems at a fixed pressure and temperature. The preprocessed data can be used in conventional EoS based calculations as an initial guess to accelerate convergence.
 Publication:

AGU Fall Meeting Abstracts
 Pub Date:
 December 2007
 Bibcode:
 2007AGUFM.H23G1699V
 Keywords:

 1847 Modeling