Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

Suzuki, Satomi; Caumon, Guillaume; Caers, Jef

doi:10.1007/s10596-007-9063-9

Cited by 111 publications

(60 citation statements)

References 18 publications

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“…In recent years, new approaches have been developed to create multiple realisations for uncertainty estimations in complex three-dimensional structural geological models (e.g., [12,13,20,23,24,[26][27][28][29]). It is expected that an application of information theoretic measures to these more complex settings will reveal novel and valuable insights into uncertainties and their potential reduction in realistic research and exploration scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Several methods addressing the analysis and visualisation of subsurface structural uncertainties have been developed in recent years (e.g., [11][12][13][23][24][25]). Structural geological models, reflecting the result of geological history over millions of years, are inherently three-dimensional and contain complex correlation structures, for example due to offset along a fault, or multiple folding patterns.…”

Section: Estimation Of Uncertainty Correlation and Reduction In A Geomentioning

confidence: 99%

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Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

Wellmann

2013

Entropy

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Abstract:The quantification and analysis of uncertainties is important in all cases where maps and models of uncertain properties are the basis for further decisions. Once these uncertainties are identified, the logical next step is to determine how they can be reduced. Information theory provides a framework for the analysis of spatial uncertainties when different subregions are considered as random variables. In the work presented here, joint entropy, conditional entropy, and mutual information are applied for a detailed analysis of spatial uncertainty correlations. The aim is to determine (i) which areas in a spatial analysis share information, and (ii) where, and by how much, additional information would reduce uncertainties. As an illustration, a typical geological example is evaluated: the case of a subsurface layer with uncertain depth, shape and thickness. Mutual information and multivariate conditional entropies are determined based on multiple simulated model realisations. Even for this simple case, the measures not only provide a clear picture of uncertainties and their correlations but also give detailed insights into the potential reduction of uncertainties at each position, given additional information at a different location. The methods are directly applicable to other types of spatial uncertainty evaluations, especially where multiple realisations of a model simulation are analysed. In summary, the application of information theoretic measures opens up the path to a better understanding of spatial uncertainties, and their relationship to information and prior knowledge, for cases where uncertain property distributions are spatially analysed and visualised in maps and models.

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Section: Discussionmentioning

confidence: 99%

Section: Estimation Of Uncertainty Correlation and Reduction In A Geomentioning

confidence: 99%

Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

Wellmann

2013

Entropy

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“…For a detailed description, we refer to [7]. For the application of MDS in uncertainty quantification, we refer to [32,35] and [8]. MDS uses the SVD to determine a low-order set of dimensionless directions in which the relative distances between the objects can be efficiently represented.…”

Section: Visualization Of Dissimilaritiesmentioning

confidence: 99%

“…[35] and [8] have used the permeability fields as a measure of dissimilarity. This has been done by defining a metric space to compare and cluster geological models that share common geological features.…”

Section: Introductionmentioning

confidence: 99%

Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach

2017

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In reservoir engineering, it is attractive to characterize the difference between reservoir models in metrics that relate to the economic performance of the reservoir as well as to the underlying geological structure. In this paper, we develop a dissimilarity measure that is based on reservoir flow patterns under a particular operational strategy. To this end, a spatial-temporal tensor representation of the reservoir flow patterns is used, while retaining the spatial structure of the flow variables. This allows reducedorder tensor representations of the dominating patterns and simple computation of a flow-induced dissimilarity measure between models. The developed tensor techniques are applied to cluster model realizations in an ensemble, based on similarity of flow characteristics.

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“…Such a set of realizations could be filtered by validation codes such as balanced restoration and geomechanical modeling (Maerten and Maerten 2006;Moretti 2008;Muron 2005). Another avenue for further progresses also covers assimilation of complex data such as reservoir production history for discarding possible structural interpretations (Suzuki et al 2008;Tarantola 2006). …”

Section: Recent and Ongoing Researchmentioning

confidence: 99%

Surface-Based 3D Modeling of Geological Structures

et al. 2009

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Building a 3D geological model from field and subsurface data is a typical task in geological studies involving natural resource evaluation and hazard assessment. However, there is quite often a gap between research papers presenting case studies or specific innovations in 3D modeling and the objectives of a typical class in 3D structural modeling, as more and more is implemented at universities. In this paper, we present general procedures and guidelines to effectively build a structural model made of faults and horizons from typical sparse data. Then we describe a typical 3D structural modeling workflow based on triangulated surfaces. Our goal is not to replace software user guides, but to provide key concepts, principles, and procedures to be applied during geomodeling tasks, with a specific focus on quality control. Electronic supplementary materialThe online version of this article (http://dx

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Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

Cited by 111 publications

References 18 publications

Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

Information Theory for Correlation Analysis and Estimation of Uncertainty Reduction in Maps and Models

Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach

Surface-Based 3D Modeling of Geological Structures

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