A parallel, multiscale approach to reservoir modeling

Abstract

With the advance of CPU power, numerical reservoir models have become an essential part of most reservoir engineering applications. These models are used for predicting future performances or determining optimal locations of infill wells. Hence in order to accurately predict, these reservoir models must be conditioned to all available data. The challenge in data integration for numerical reservoir models lies in the fact that each data has its own resolution and area of coverage. The most common data for reservoir characterization are; well-log/core data, seismic data and production data. Most current approaches to data integration are hierarchical. Fine scale models are used for integrating well-log/core and seismic data while coarse models are used to integrate mostly production data. The drawback of such a hierarchical approach is such that once the scale is changed, data conditioning, maintained in the previous scale, is lost. In this paper, we review a general algorithm as a solution to the multi-scale data integration. Instead of proceeding in a hierarchical fashion, a fine model and a coarse model is kept in parallel throughout the entire characterization process. The link between the fine scale and the coarse scale is provided by non-uniform upscaling. An optimization procedure determines the optimal gridding parameters that provide the smallest possible mismatch between fine and coarse scale reservoir models. A synthetic example application is given and demonstration of the methodology. The upgridding is accomplish by a static gridding algorithm, 3DDEGA. This algorithm aims at preserving geology by minimizing heterogeneity within a coarse grid block. The coarse grids are provided in a corner-point geometry fashion, hence this allows for accurate description of the reservoir with fewer number of grid blocks.

Modeling transient groundwater flow by coupling ensemble Kalman filtering and upscaling

“…The main shortcoming of this approach is that the inverse modeling is performed on a crude upscaled model, resulting in a downscaled model that will not honor the state data accurately. Tureyen and Caers [2005] proposed the calibration of the fine‐scale conductivity field by gradual deformation [ Hu , 2000; Capilla and Llopis‐Albert , 2009], but instead of solving the flow equation at the fine scale they used an approximate solution after upscaling the hydraulic conductivity field to a coarse scale. This process requires an upscaling for each iteration of the gradual deformation algorithm, which is also time‐consuming, although they avoid the fine‐scale flow solution.…”

Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers

“…The transition between both stages can be done by adapting the whole swarm to the new augmented base or by just passing the global best obtained in the first step and generating randomly the rest of the particles in the expanded search space. Multiscale approaches have already been used in reservoir modeling and history marching problems (see for instance Tureyen and Caers, 2005;Aanonsen and Eydinov, 2006). Figure 7 shows the median convergence curve for different family members obtained by mutiscale inversion with 10 and 20 PCA terms respectively using a swarm of 20 particles.…”

Study of heterogeneity loss in upscaling of geological maps by introducing a cluster-based heterogeneity number

“…They applied the production log data in Lorenz plot to find the V DP . Tureyen and Caers [20] proposed a heterogeneity index for upgridding the geostatistical realizations. In their works, the coarse cell boundaries were determined by minimizing the heterogeneity over the internal fine grids.…”