A Practical Approach for Assisted History Matching Using Grid Coarsening and Streamline-Based Inversion: Experiences in a Giant Carbonate Reservoir

摘要

Flow simulation of multimillion grid cell models with hundreds of wells and decades long production history can be extremely time-consuming. This often limits the applicability of assisted history matching techniques. A pragmatic solution to this problem is grid coarsening which is now embedded in many commercial reservoir simulators. Instead of up-scaling geological models in external packages, grid cells are automatically amalgamated within the simulator while preserving flux distribution and reducing the total number of active cells. The resulting speedup can be significant, often only at small loss of accuracy. Both characteristics are essential elements of any inversion technique in a multimillion grid cell environment. For water-flood history matching, we have utilized a commercial finite-volume simulator and the streamline-based generalized travel time inversion whereby water-cut behavior is matched by adjusting inter-well permeabilities. To apply the assisted history matching technique on high resolution models, a flux reconstruction method is devised which makes full use of the benefits of automatic grid coarsening. It approximates fluxes in the original geological grid by recalculating transmissibility at the fine scale and redistributing coarse scale fluxes accordingly. The inversion is thus conducted on the fine scale grid, while the forward simulation model uses the coarse grid. While solving for an inexpensive coarse model, the calibration takes into account fine grid resolution. Upscaling is not required and the degree of coarsening can be adjusted during the matching process. The proposed method was successfully tested on a supergiant carbonate oilfield with about hundred wells and large-scale water injection. The history match improved dramatically at relatively low numerical cost, which also allowed for investigating multiple sensitivities. The results were verified against non-coarsened model. The significant increase in efficiency makes this a potential method of choice for cases, where previously assisted history matching techniques could not be deployed due to excessive run times.