Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation

Lee, Si-Yong; Carle, Steven F.; Fogg, Graham E.

doi:10.1016/j.advwatres.2007.03.005

Cited by 145 publications

(104 citation statements)

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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%

“…Outcrop data were also used by Dai et al [2005] for a better definition of transition probability. Recent studies [Maji et al, 2006;Lee et al, 2007] demonstrate that the TP/MC method outperforms conventional geostatistical methods by reconstructing the linkage between lithology and contaminant plumes.…”

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]. An advantage of the TP/MC method relative to the conventional geostatistical methods is that it can incorporate geological information such as mean lengths and directional orientation of lithofacies [Carle andFogg, 1996, 1997].…”

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%

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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“…Several modelling strategies exist, for example structure-imitating methods aim to reproduce the spatial patterns and distribution of the deposits without explicitly considering sedimentary processes (Koltermann and Gorelick, 1996). These have been widely used to reconstruct or simulate facies distributions in the subsurface, mostly in relation to hydrocarbon reservoirs or aquifers (for example: Johnson and Dreisss, 1989;Langlais and Doyle, 1993;Gotway and Rutherford, 1994;de Marsily et al, 1998 andLee et al, 2007). However, examples of their application to coal zones are limited (Falivene et al, 2007b;Heriawan and Koike, 2008b;Deutsch and Wilde, 2013).…”

Section: Coal Resources Reserves and 3d Facies Modelsmentioning

confidence: 99%

Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Falivene

Cabrera

Sáez

2014

International Journal of Coal Geology

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Forecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, threedimensional geostatistical methods are used to populate facies distribution within a densely sampled heterogeneous coal zone in the As Pontes basin (NW Spain). Coal distribution in this zone is mainly characterized by coal-dominated areas in the central parts of the basin interfingering with terrigenousdominated alluvial fan zones at the basin margins. Resultant models are applied to forecast coal resources and reserves. Predictions using subsets of available well data are also generated to understand performance under limited data constraints. Facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a block can be recovered or not, and b) the method capability to reproduce areal trends in coal proportions and splitting between coal-dominated and terrigenous-dominated areas of the basin. Reserves predictions differ between the simulation methods, even with dense conditioning datasets. Simulation methods are ranked according to the correlation of their outputs with predictions to the directly interpolated coal proportions: with lowdensity datasets sequential indicator simulation with trends yields the best correlation, with highdensity datasets sequential indicator simulation with post-processing using maximum a posteriori selection yields the best correlation. AbstractForecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, three-dimensional geostatistical methods are used to populate facies distribution within a densely sampled heterogeneous coal zone in the As Pontes basin (NW Spain). Coal distribution in this zone is mainly characterized by coaldominated areas in the central parts of the basin interfingering with terrigenousdominated alluvial fan zones at the basin margins. Resultant models are applied to forecast coal resources and reserves. Predictions using subsets of available well data are also generated to understand performance under limited data constraints.Facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a ...

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“…dell 'Arciprete et al (2012) show through a comparison between sequential indicator, transition probability and Multiple-Point Statistics (MPS) simulations, that the most realistic geological realizations of alluvial sediments are obtained when facies details are simulated within previously established structural elements. Many experiments have also been conducted for other types of environments (Zinn and Harvey, 2003;Falivene et al, 2006;Lee et al, 2007;Zhang et al, 2013;Siirila-Woodburn and Maxwell, 2015). In general, the main objective of all these studies was to compare the flow and transport properties (equivalent conductivity, macrodispersivity, etc.)…”

Section: Introductionmentioning

confidence: 99%

Influence of conceptual model uncertainty on contaminant transport forecasting in braided river aquifers

Pirot

Renard

Huber

et al. 2015

Journal of Hydrology

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Keywords:Uncertainty Contaminant transport Modeling Braided river Aquifer Groundwater s u m m a r y Hydrogeologist are commonly confronted to field data scarcity. An interesting way to compensate this data paucity, is to use analog data. Then the questions of prediction accuracy and uncertainty assessment when using analog data shall be raised. These questions are investigated in the current paper in the case of contaminant transport forecasting in braided river aquifers. In using analog data from the literature, multiple unconditional geological realizations are produced following different geological conceptual models (Multi-Gaussian, Object-based, Pseudo-Genetic). These petrophysical realizations are tested in a contaminant transport problem based on the MADE-II tracer experiment dataset. The simulations show that reasonable contaminant transport predictions can be achieved using analog data. The initial concentration conditions and location regarding the conductivity heterogeneity field have a stronger influence on the plume behavior than the resulting equivalent permeability. The results also underline the necessity to include a wide variety of geological conceptual models and not to restrain parameter space exploration within each concept as long as no field data allows for conceptual model or parameter value falsification.

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Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation

Cited by 145 publications

References 50 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

Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Influence of conceptual model uncertainty on contaminant transport forecasting in braided river aquifers

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