The influence of conceptual model of sedimentary formation hydraulic heterogeneity on contaminant transport simulation

Pozdniakov, S.; Bakshevskaya, V. A.; Krohicheva, I. V.; Danilov, V. V.; Zubkov, A.A.

doi:10.3103/s0145875212010097

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…-The output of to is provided in an open data format, making the presented workflow and tools versatile and potentially useful for a multitude of applications, e.g., aiding in fundamental research (McKenna et al, 2003;Pozdniakov et al, 2012), offering easier and straightforward integration of subsurface heterogeneous information (Walther et al, 2012b), or increasing prediction quality (Sutanudjaja et al, 2011;de Hoyos et al, 2012). Respective readers and conversion methods can easily be written following the VTK framework documentation.…”

Section: Discussionmentioning

confidence: 99%

“…The workflows use external open-source meshing tools (e.g., GMSH, Geuzaine and Remacle, 2009 3 , or TetGen, Si, 2015 4 ), or an interface to the Ansys 5 modeling environment. Recently, Qu et al (2015) offered a method to generate volumetric gridded fault zones by adding elements in the respective areas to the existing grids. This method, although shown to be applicable on a multitude of setups, will require an existing base mesh of good quality, requires the use of additional software, and is not open-sourced.…”

Section: Previous Work and The Literaturementioning

confidence: 99%

See 1 more Smart Citation

GO2OGS: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

Fischer¹,

Walther²,

Sattler³

et al. 2015

Preprint

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Abstract. We offer a versatile workflow to convert geological models built with the software Paradigm™ GOCAD© into the open-source VTU format for the usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed as a way of communication between the diverse tools of the various disciplines. Our approach offers an open-source, platform independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies. With two application examples in the Thuringian Syncline, we show how a heterogeneous geological GOCAD model including multiple layers and faults can be used for numerical groundwater flow modelling. The presented workflow offers the chance to incorporate increasingly detailed data, utilizing growing availability of computational power to simulate numerical models.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Work and The Literaturementioning

confidence: 99%

GO2OGS: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

Fischer¹,

Walther²,

Sattler³

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

et al. 2015

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Abstract. We offer a versatile workflow to convert geological models built with the Paradigm ™ GOCAD © (Geological Object Computer Aided Design) software into the opensource VTU (Visualization Toolkit unstructured grid) format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed as a way of communication between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of environmental studies. With two application examples in the Thuringian Syncline, we show how a heterogeneous geological GO-CAD model including multiple layers and faults can be used for numerical groundwater flow modeling, in our case employing the OpenGeoSys open-source numerical toolbox for groundwater flow simulations. The presented workflow offers the chance to incorporate increasingly detailed data, utilizing the growing availability of computational power to simulate numerical models.

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“…• Three-dimensional (3-D) transition probability models of spatial variability are readily developed from 1-D Markov chains along principal stratigraphic directions (Carle and Fogg, 1997). • Continuous-lag Markov chains have been found suitable for 3-D modeling of vertical and lateral spatial transitioning among geo-or hydro-facies (Carle 1996;Carle and Fogg, 1996;Carle and Fogg, 1997;Carle et al, 1998;Fogg et al, 1998;Zhang and Fogg, 2003;Proce et al, 2004;Ye and Khaleel, 2008;Engdahl et al, 2010a;Bianchi et al, 2011;Pozdniakov et al, 2012;Purkis et al, 2012;Bakshevskaia and Pozdniakov, 2016;Krage et al, 2016;Sartore et al, 2016;Zhu et al, 2016a;Meirovitz et al, 2017;Guo et al, 2019b).…”

Section: Transition Probability-based Indicator Geostatisticsmentioning

confidence: 99%

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Carle

Fogg

2020

Front. Earth Sci.

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Uncertain or indirect "soft" data, such as geologic interpretation, driller's logs, geophysical logs or imaging, offer potential constraints or "soft conditioning" to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data "hardness" parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or "prior realization") can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

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The influence of conceptual model of sedimentary formation hydraulic heterogeneity on contaminant transport simulation

Cited by 6 publications

References 12 publications

GO2OGS: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

GO2OGS: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

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