Issues in characterizing heterogeneity and connectivity in non-multiGaussian media

Kerrou, Jaouher; Renard, Philippe; Franssen, Harrie‐Jan Hendricks; Lunati, Ivan

doi:10.1016/j.advwatres.2007.07.002

Cited by 73 publications

(54 citation statements)

References 36 publications

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“…Both aspects are likely related to spatial correlations and interactions between fractures [14]. The capacity of DFN models to reproduce flow channeling is essential for a good estimation of hydraulic properties [15][16][17].…”

Section: Discrete Fracture Network Modelmentioning

confidence: 99%

Topological impact of constrained fracture growth

et al. 2015

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The topology of two discrete fracture network models is compared to investigate the impact of constrained fracture growth. In the Poissonian discrete fracture network model the fractures are assigned length, position and orientation independent of all other fractures, while in the mechanical discrete fracture network model the fractures grow and the growth can be limited by the presence of other fractures. The topology is found to be impacted by both the choice of model, as well as the choice of rules for the mechanical model. A significant difference is the degree mixing. In two dimensions the Poissonian model results in assortative networks, while the mechanical model results in disassortative networks. In three dimensions both models produce disassortative networks, but the disassortative mixing is strongest for the mechanical model.

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Section: Discrete Fracture Network Modelmentioning

confidence: 99%

Topological impact of constrained fracture growth

et al. 2015

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“…To characterize the spatial variability of the hydraulic conductivity, random space function (RSF) is often adopted (Ezzedine and Rubin 1996;Feyen et al 2003;Franssen et al 2003;Kerrou et al 2008;Hassan et al 2009;Liang et al 2009Liang et al , 2010. However, scarcity of measurements leads to generating spatial structure pattern of hydraulic conductivity at a few sampling location, which arouse uncertainty of spatial hydraulic conductivity distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian approach to quantify parameter uncertainty and impacts on predictive flow and mass transport in heterogeneous aquifer

Liang

Zeng

Shen

et al. 2013

Int. J. Environ. Sci. Technol.

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Groundwater flow and mass transport predictions are subjected to uncertainty due to heterogeneity of hydraulic conductivity, whose variability in space is considerably higher than that of other hydraulic properties relevant to groundwater flow. To characterize the distribution of hydraulic conductivity, random space function (RSF) is often used. The Bayesian approach was applied to quantitatively study the effect of parameter uncertainty in RSF on a hypothetical two-dimensional uniform groundwater flow and mass transport. Specifically, the parameter uncertainty transmitted to macrodispersion in mass transport model was also inferred. The results showed that the posterior probability distributions of parameters were updated after Bayesian inference. The numerical experiments indicated that the overall predictive uncertainty was increased with simulating time along the flow direction. As to the relative contribution of the two types of uncertainty, it indicated that parametric uncertainty was a little more important than stochastic uncertainty for the predictive uncertainty of hydraulic head. When the uncertainty of hydraulic head as well as macrodispersion was transported to mass transport model, a much bigger contribution of stochastic uncertainty was observed. Therefore, parametric uncertainty should not be neglected during the process of subsurface simulation.

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“…These results demonstrated that the reproduction of the connectivity is of significance in practice. For the inverse conditioning, Kerrou et al (2008) applied the self-calibration (Gómez-Hernández et al, 1997) method on a fluvial-sediment aquifer with a wrong prior model (i.e. multiGasussian instead of non-multiGaussian) and concluded that the channel structures cannot be retrieved, even when a large number of direct and indirect data are used for conditioning.…”

Section: Introductionmentioning

confidence: 99%

Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter

Li¹,

Zhou²,

Franssen³

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. The normal-score ensemble Kalman filter (NSEnKF) is tested on a synthetic aquifer characterized by the presence of channels with a bimodal distribution of its hydraulic conductivities. This is a clear example of an aquifer that cannot be characterized by a multiGaussian distribution. Fourteen scenarios are analyzed which differ among them in one or various of the following aspects: the prior random function model, the boundary conditions of the flow problem, the number of piezometers used in the assimilation process, or the use of covariance localization in the implementation of the Kalman filter. The performance of the NS-EnKF is evaluated through the ensemble mean and variance maps, the connectivity patterns of the individual conductivity realizations and the degree of reproduction of the piezometric heads. The results show that (i) the localized NS-EnKF can characterize the non-multiGaussian underlying hydraulic distribution even when an erroneous prior random function model is used, (ii) localization plays an important role to prevent filter inbreeding and results in a better logconductivity characterization, and (iii) the NS-EnKF works equally well under very different flow configurations.

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Issues in characterizing heterogeneity and connectivity in non-multiGaussian media

Cited by 73 publications

References 36 publications

Topological impact of constrained fracture growth

Topological impact of constrained fracture growth

Bayesian approach to quantify parameter uncertainty and impacts on predictive flow and mass transport in heterogeneous aquifer

Groundwater flow inverse modeling in non-MultiGaussian media: performance assessment of the normal-score Ensemble Kalman Filter

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