Effect of connectivity misrepresentation on accuracy of upscaled models in oil recovery by CO₂ injection

Abstract

An upscaling method such as renormalization converts a detailed geological model to a coarse one. Although flow equations can be solved faster on a coarse model, its results have more errors. Numerical dispersion, heterogeneity loss, and connectivity misrepresentation are the factors responsible for errors. Connectivity has a great effect on the fluid distribution and leakage pathways in EOR processes or CO2 storage. This paper deals with the description and quantification of connectivity misrepresentation in the upscaling process. For detection of high‐flow regions, the flow equations are solved under simplified single‐phase conditions. These regions are recognized as the cells whose fluxes are greater than a cut‐off. The connectivity is investigated by checking whether the high‐flux region forms a spanning cluster across the model. An indicator called the flux connectivity distortion is defined to measure the connectivity misrepresentation of coarse grids. This indicator is shown to have a strong correlation with the oil saturation error. For renormalization method, this relation helps to propose an upper limit of coarsening beyond which the coarse model results are not reliable. Once the proper sizes of coarse blocks are determined, flow behavior can be simulated accurately in a project of CO2‐EOR or CO2 sequestration. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

Analysis of the impacts of relative permeability and mobility ratio on heterogeneity loss error during upscaling of geological models

“…However, the NRMSE is calculated after the simulation processes. Heterogeneity loss analysis results in presenting the relationship between the flow performance error (NRMSE) and heterogeneity loss due to the upscaling in a chart, whose slope represents the magnitude of the homogenization error (Ganjeh-Ghazvini et al, 2015a). A more accurate coarse model results in less heterogeneity loss error and a smaller slope.…”

“…The upper limit of coarsening is an approximate value (as in this research, the effect of numerical error is eliminated to investigate the heterogeneity loss behavior), unless the numerical dispersion would be very small and/or diminishes someway or the total upscaling error would have been reduced due to the opposing nature of these two types of errors (Preux, 2016;Sablok and Aziz, 2008). To have a reasonable result, the NRMSE should not exceed of 0.085 (Ganjeh-Ghazvini et al, 2015a). Based on the slopes shown in Figure 8 and the critical value of NRMSE, the results of a coarse model are trustworthy if the ratio of Ht f…”

“…The number of grid blocks must, therefore, be reduced by upscaling which converts the geological model to simulation one. The upscaling process reduces the accuracy as it saves time, so it must be done in such a way to balance between these two (Ganjeh-Ghazvini et al, 2015a;Misaghian et al, 2018).…”

“…There are relatively few studies in the area of upscaling errors which have not been investigated in detail. Three types of errors which can be caused by upscaling are numerical dispersion, heterogeneity loss and connectivity misrepresentation (Ganjeh-Ghazvini, 2019;Ganjeh-Ghazvini et al, 2015a). The latter mostly occurs in the gas-oil displacement.…”

Simulation study of water and gas injection process into the Azadegan reservoir for enhanced oil recovery

“…There are different methods to increase oil production and each method can be implemented in certain conditions. 5 The methods have certain advantages and limitations. The methods include: gas injection (hydrocarbons, nitrogen and carbon dioxide) in miscible and immiscible form, polymer injection, surfactant injection, oil combustion, low salinity water injection, steam injection, high temperature water injection.…”