HR: 1340h
AN: H23G-1699    [Abstracts]
TI: General Thermodynamic Parameterization for multi-component multiphase flow
AU: * Voskov, D
EM: dvoskov@stanford.edu
AF: Energy Resources Engineering Department, Stanford University, 367 Panama Street, 065, Stanford, CA 94025, United States
AU: Tchelepi, H
EM: tchelepi@stanford.edu
AF: Energy Resources Engineering Department, Stanford University, 367 Panama Street, 065, Stanford, CA 94025, United States
AB: We present a general parameterization of the thermodynamic behavior of multiphase, multi-component systems. The phase behavior in the compositional space is represented using a low dimensional tie-simplex parameterization. For example, these are tie-lines and tie-triangles for two- and three-phase 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. Large-scale 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 tie-simplex 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.
DE: 1847 Modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting