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

Ganjeh-Ghazvini, Mostafa; Masihi, Mohsen; Baghalha, Morteza

doi:10.1016/j.physa.2015.05.010

Cited by 7 publications

(7 citation statements)

References 52 publications

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“…The coarse model is considered the base; every cell is then divided into some grids. The result, called the refined upscaled (RU) model, has cells as small as the fine grid blocks . The RU model inherits its properties from the base (parent) coarse model.…”

Section: Description and Quantification Of Errors In Upscaling Performentioning

confidence: 99%

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

2015

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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

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Section: Description and Quantification Of Errors In Upscaling Performentioning

confidence: 99%

“…The coarse model results usually have more errors than those of the reference fine model. Two factors cause the error: the numerical dispersion and heterogeneity loss . A coarse model suffers from the numerical dispersion more than a fine model because this error depends on the block size.…”

Section: Introductionmentioning

confidence: 99%

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

2015

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“…The latter mostly occurs in the gas-oil displacement. Due to the low viscosity of the gas, a slight change in the flow paths results in large errors (Ganjeh-Ghazvini et al, 2015b). Preux et al (2016) performed the connectivity analysis based on the shortest paths connecting wells both for a water injection and a gas injection process so as to find an appropriate coarse model.…”

Section: Introductionmentioning

confidence: 99%

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

Darban

Ghaedi

Qajar

2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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The detailed geological fine grids are upscaled to create reliably sized simulation coarse models to solve flow equations in a more efficient way. Any upscaling process results in a loss of accuracy, along with an increase of errors. Numerical dispersion, heterogeneity loss, and connectivity misrepresentation are responsible for the upscaling errors. Recognizing the source of each error, and the behavior of influential factors through upscaling process could provide an optimum level of upscaling and an evaluation of upscaling methods’ accuracy. Despite the importance of upscaling error, little attention has been paid to this subject. This paper represents a rigorous analysis of the heterogeneity loss behavior associated with the relative permeability contrast and the mobility ratio under a waterflooding process. For this purpose, heterogeneous fine grid models are constructed by the fractional Brownian motion process. The models are upscaled by three upscaling factors. The models achieved are implemented to eliminate the impact of numerical error among upscaling errors in order to focus strictly on heterogeneity loss. Water–oil displacement simulation is then performed on fine and corresponding refined upscaled models at three different ratios of relative permeabilities and mobility ratios. In the next stage, the relation between flow performance error and heterogeneity loss is investigated by the heterogeneity loss plot. The slope of this plot provides the reservoir engineer an insight to evaluate the performance of upscaling methods and the behavior of the influential factors on upscaling errors. Moreover, by using the heterogeneity loss plot for each ratio, a limit of coarsening is presented. Based on the results, the heterogeneity loss error is affected more by the mobility ratio contrast than the relative permeability difference. Also, it is demonstrated that water-wet reservoirs with light oil are more sensitive to the level of upscaling.

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“…Regardless of the approach chosen, the upscaling step leads to a loss of information. Different criteria have been proposed to quantify the error introduced by the upscaling step [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Transmissibility Upscaling on Unstructured Grids for Highly Heterogeneous Reservoirs

Guérillot

Bruyelle

2019

Water

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One critical point of modeling of flow in porous media is the capacity to consider parameters that are highly variable in space. It is then very challenging to simulate numerically fluid flow on such heterogeneous porous media. The continuous increase in computing power makes it possible to integrate smaller and smaller heterogeneities into geological models of up to tens of millions of cells. On such meshes, despite computer performance, multi-phase flow equations cannot be solved in an acceptable time for hydrogeologists and reservoir engineers, especially when the modeling considers several components in each fluid and when taking into account rock-fluid interactions. Taking average reservoir properties is a common approach to reducing mesh size. During the last decades, many authors studied the upscaling topic. Two different ways have been investigated to upscale the absolute permeability: (1) an average of the permeability for each cell, which is then used for standard transmissibility calculation, or (2) computing directly the upscaled transmissibility values using the high-resolution permeability values. This paper is related to the second approach. The proposed method uses the half-block approach and combines the finite volume principles with algebraic methods to provide an upper and a lower bound of the upscaled transmissibility values. An application on an extracted map of the SPE10 model shows that this approach is more accurate and faster than the classical transmissibility upscaling method based on flow simulation. This approach keeps the contrast of transmissibility values observed at the high-resolution geological scale and improves the accuracy of field-scale flow simulation for highly heterogeneous reservoirs. Moreover, the upper and lower bounds delivered by the algebraic method allow checking the quality of the upscaling and the gridding.

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

Cited by 7 publications

References 52 publications

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

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

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

Transmissibility Upscaling on Unstructured Grids for Highly Heterogeneous Reservoirs

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

Cited by 7 publications

References 52 publications

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

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

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

Transmissibility Upscaling on Unstructured Grids for Highly Heterogeneous Reservoirs

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Product

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

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