A Multidomain Approach to Multiscale Compositional Reservoir Simulation
Mohammed F. Al-Matrouk, Mohammed Al Kobaisi, Hossein Kazemi, Erdal H. Ozkan · 2009
Abstract This paper presents a multidomain (MD) computational approach to multiscale (MS) compositional reservoir simulation amenable to parallel processing. The main objective is to minimize the upscaling process and preserve the pertinent flow characteristics of the fine-scale geological features of the reservoir. The preservation of the fine-scale reservoir characteristics yields higher-accuracy solution compared to an upscaled model. The computing algorithm involves breaking down the fine-grid (FG) computing mesh into an underlying coarse-grid (CG) subdomain. Unlike other MS techniques, the CG subdomain only serves as an assemblage of the FG cells and not as a computational CG overlaying the FG cells. The flow equations are then restructured into a global pressure equation which is solved on the FG using a large time step without solving for the compositions, saturations, etc. The convergence tolerance for the global pressure solution is soft (no iterations) to reduce computing time. The pressure solution is then used to calculate the molar flux of components only at the FG boundaries of the CG subdomain cells. Finally, each CG subdomain subject to its boundary conditions is solved independently using a smaller time step to compute cell pressures, compositions, and phase saturations. This procedure is amenable to parallel processing. This MS scheme was applied to time domain, in which the global pressure solution is obtained with a larger time step than in the local FG solution. This means that the molar flux boundary condition at the CG subdomain boundaries is not updated every time step, thus, buying additional computational advantage. This new technique was tested on several volatile oil and gas-condensate systems and excellent agreement was obtained with FG simulation with about 60% reduction in computing time using a single processor. Much greater time reduction is expected in a multi processing environment.