Quantification of Uncertainty in Coarse-Scale Relative Permeabilities Due to Sub-Grid Heterogeneity
نویسنده
چکیده
This paper addresses two issues. Firstly, because the coarsescale model inevitably misses out subgrid heterogeneity, physical dispersion is ignored in the simulation. Secondly, the smallscale heterogeneity is not explicitly known and can only be inferred by historymatching. To solve these problems, local features in the coarsescale relative permeability curves were adjusted in historymatching to capture the effect of physical dispersion and to compensate for the effect of numerical dispersion.
منابع مشابه
Quantification of Uncertainty Due to Subgrid Heterogeneity in Reservoir Models
Due to the lack of data, a reservoir engineer needs to calibrate unknown petrophysical parameters based on production history. However, because the observations cannot constrain all the subsurface properties over a field, production forecasts for reservoirs are essentially uncertain. In general, many parameters of the model must be adjusted in the historymatching process, and the amount of comp...
متن کاملQuantification of Uncertainty in Relative Permeability for Coarse-Scale Reservoir Simulation
Reservoir simulation to predict production performance requires two steps: one is history-matching, and the other is uncertainty quantification in forecasting. In the process of history-matching, rock relative permeability curves are often altered to reproduce production data. However, guidelines for changing the shape of the curves have not been clearly established. The aim of this paper is to...
متن کاملSingle-phase Near-well Permeability Upscaling and Productivity Index Calculation Methods
Reservoir models with many grid blocks suffer from long run time; it is hence important to deliberate a method to remedy this drawback. Usual upscaling methods are proved to fail to reproduce fine grid model behaviors in coarse grid models in well proximity. This is attributed to rapid pressure changes in the near-well region. Standard permeability upscaling methods are limited to systems with ...
متن کاملA machine learning approach for efficient uncertainty quantification using multiscale methods
Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predicto...
متن کاملDynamic Upscaling from the Pore to the Reservoir Scale
We propose a dynamic three-stage upscaling methodology from the pore scale to the reservoir scale. In traditional upscaling approaches, simulations at smaller scales are used to compute effective transport properties that are used as look-up values in a larger scale simulation. In the proposed approach, simulations at different scales are performed simultaneously. Effective properties are found...
متن کامل