Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

Feyen, Luc; Caers, Jef

doi:10.1016/j.advwatres.2005.08.002

Cited by 139 publications

(85 citation statements)

References 32 publications

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“…There have been two ways of handling hydraulic parameters for lithofacies in the TP/MC method. One approach uses effective parameter values for each lithofacies [e.g., Fogg et al, 1998;Zhang et al, 2006;Lee et al, 2007]; the other approach treats the parameters as random variables [e.g., Haldorsen and Damsleth, 1990;Tyler et al, 1994;Bi and Oliver, 2000;Feyen and Caers, 2006]. Both approaches can be implemented for the soil classes in a straightforward manner on the basis of decades of vadose zone studies.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional geostatistical methods (e.g., kriging) use the two-point variogram (or covariance) to describe spatial structures; recently the three-point variogram has been advocated [Strebelle, 2002;Krishnan and Journel, 2003;Journel and Zhang, 2006;Feyen and Caers, 2006]. An alternate geostatistical method, one that does not use a variogram, uses transition probability and Markov chains (TP/MC) to describe spatial structures of categorical data [e.g., Carle andFogg, 1996, 1997;Fogg et al, 1998;Carle, 1999;Weissmann et al, 1999;Li et al, 1999;Ritzi, 2000;Elfeki and Dekking, 2001;Lu and Zhang, 2002;Park et al, 2004;Dai et al, 2005;Sivakumar et al, 2005;Maji et al, 2006;Zhang et al, 2006;Li, 2007aLi, , 2007bLee et al, 2007;Dai et al, 2007;Sun et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

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A Markov chain model for characterizing medium heterogeneity and sediment layering structure

Khaleel

2008

Water Resources Research

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[1] By leveraging use of ''soft'' data (e.g., initial moisture content, q i ), this study applies the transition probability (TP) based Markov chain (MC) model to sediment textural classes for characterizing the medium heterogeneity and sediment layering structure. The TP/MC method is evaluated by simulating the vadose zone moisture movement at a field site, where the stratigraphy consists of imperfectly stratified soil layers. Soil heterogeneity is characterized via spatial variability of the geometry of soil textural classes. When the q i measurements, which carry signature about medium heterogeneity and stratigraphy, are not included in the TP/MC model, it is not possible to identify the horizontal TP. The q i measurements, when transformed into soil classes, are necessary in mapping the soil layering structure prevalent at the site. The soil hydraulic parameters for each soil class are treated deterministically and are estimated on the basis of core samples. To evaluate uncertainty in characterizing geometry of the soil classes, multiple conditional realizations of the soil classes are generated. A Monte Carlo simulation shows that the simulated mean moisture contents agree well with corresponding field observations. The observed splitting of the moisture plume in a coarse sand layer that is sandwiched between two fine-textured layers, the southeastward movement of the plume during the redistribution period, and the near-zero fluid flux below the bottom fine layer are adequately simulated. Spatial variability of the field-measured moisture content is sufficiently captured by the 95% confidence intervals calculated from the Monte Carlo simulations. Investigating the effect of data conditioning on the simulated results shows that a reduction of conditioning data does not necessarily deteriorate simulation results if other conditioning data exist within the mean length of the soil classes. The TP/MC method is flexible so that other types of site characterization data (e.g., geophysical data) can be incorporated as they become available.Citation: Ye, M., and R. Khaleel (2008), A Markov chain model for characterizing medium heterogeneity and sediment layering structure, Water Resour. Res., 44, W09427,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Markov chain model for characterizing medium heterogeneity and sediment layering structure

Khaleel

2008

Water Resources Research

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“…Moreover, variograms, as a limited and parsimonious mathematical tool, cannot take full advantage of the possibly rich amount of geological information from outcrops (Caers and Zhang, 2004). Multiple-point geostatistics (Strebelle 2000;Strebelle 2002;Strebelle et al 2002;Caers and Zhang 2003;Feyen and Caers , 2006) aims to overcome the limitations of the variogram. The premise of multiple-point geostatistics is to move beyond two-point correlations between variables and to obtain (cross) correlation moments at multiple locations at a time (Strebelle and Journel, 2001).…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple-Point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer

Huysmans

Dassargues

2010

Quantitative Geology and Geostatistics

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“…Multiple-point geostatistics offers a solution to this problem by imitating complex spatial patterns by using a training image (Caers and Zhang, 2004;Hu and Chugunova, 2008). Examples of applications of multiple-point geostatistics in hydrogeology include Feyen and Caers (2006), Ronayne and others (2008), and Huysmans and Dassargues (2009).…”

Section: Introductionmentioning

confidence: 99%

A multiple-point geostatistical method for characterizing uncertainty of subsurface alluvial units and its effects on flow and transport

Cronkite-Ratcliff¹,

Phelps²,

Boucher³

2012

Open-File Report

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Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

Cited by 139 publications

References 32 publications

A Markov chain model for characterizing medium heterogeneity and sediment layering structure

A Markov chain model for characterizing medium heterogeneity and sediment layering structure

Application of Multiple-Point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer

A multiple-point geostatistical method for characterizing uncertainty of subsurface alluvial units and its effects on flow and transport

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