Abstract
Many improved-oil-recovery (IOR)/enhanced-oil-recovery (EOR) processes such as chemical, miscible, and steamflooding are often associated with complex flow mechanisms that manifest at the displacement front. Viscous fingering, polymer/surfactant dilution, and mixing effects are some of these mechanisms. Accurate modeling of these phenomena requires simulations on high-resolution grids to properly capture the physics in the vicinity of the displacement front. Unfortunately, high grid resolutions incur longer simulation times. Thus, past efforts at running full-field gas or chemical EOR (CEOR) simulations were frequently deemed impractical.
The advancement in computational power from software, hardware, and parallelism has indeed pushed the limits toward higher-resolution simulations. However, this may not be practical in work flows that require simulations on many models to manage uncertainties. Dynamic gridding is one approach that attempts to adjust the grid resolution as needed during the run time. No a priori knowledge is assumed regarding the fluid-flow pathways. The simulator can track the location of the displacement front, refine the neighborhood cells, and later coarsen them back as the front progresses. The advantage is reducing the number of gridblocks and, therefore, the computational time, compared with the fully refined grid, while preserving the fluid-flow physics. Although this technology is not new in reservoir simulation, there are persisting challenges in the existing methods related to the computational overhead associated with cell remapping, transmissibility recalculation, and grid upscaling and downscaling.
A new dynamic-gridding functionality has successfully been implemented into our in-house simulator. The key achievements are: (1) eliminating grid remapping and transmissibility recalculation at the run time, (2) capturing heterogeneity associated with all levels of grid refinements, (3) modeling complex geology with nonuniform gridding, and (4) tracking multiple fronts associated with surfactant-polymer (SP) and chase-water slugs. We discuss how we overcame the bottlenecks to leverage this technology from prototypes to complex cases. We also demonstrate our method on prototypes and field cases under CEOR recovery processes.