Towards Stochastic Time-Varying Geological Modeling

Caumon, Guillaume

doi:10.1007/s11004-010-9280-y

Cited by 65 publications

(34 citation statements)

References 75 publications

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“…In practice, significant uncertainty may exist due to the lack of data, so methods to simulate possible stratigraphic geometries should also be considered (Goff, 2000;Abrahamsen et al, 1992;Caumon, 2010;Cherpeau and Caumon, 2015). Indeed, when the algorithm outputs a hiatus, there is no clue to identify the position of the hiatus (for example the erosion line) between the wells.…”

Section: Resultsmentioning

confidence: 99%

Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

Edwards

Lallier

Caumon

et al. 2018

Computers & Geosciences

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We discuss the sampling and the volumetric impact of stratigraphic correlation uncertainties in basins and reservoirs. From an input set of wells, we evaluate the probability for two stratigraphic units to be associated using an analog stratigraphic model. In the presence of multiple wells, this method sequentially updates a stratigraphic column defining the stratigraphic layering for each possible set of realizations. The resulting correlations are then used to create stratigraphic grids in three dimensions. We apply this method on a set of synthetic wells sampling a forward stratigraphic model built with Dionisos. To perform cross-validation of the method, we introduce a distance comparing the relative geological time of two models for each geographic position, and we compare the models in terms of volumes. Results show the ability of the method to automatically generate stratigraphic correlation scenarios, and also highlight some challenges when sampling stratigraphic uncertainties from multiple wells.

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Section: Resultsmentioning

confidence: 99%

Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

Edwards

Lallier

Caumon

et al. 2018

Computers & Geosciences

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“…the fact that several possible fine-scale descriptions can explain the same coarsescale observations (e.g. Caumon, 2010;Journel and Huijbregts, 1978;Tarantola, 2006).…”

Section: Introductionmentioning

confidence: 93%

Sampling the uncertainty associated with segmented normal fault interpretation using a stochastic downscaling method

Julio¹,

Caumon²,

Ford³

2015

Tectonophysics

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“…Although it is proved that accounting for rock mechanic parameters in the restoration offers greater precision and provides better accuracy for describing strain and stress fields (Durand-Riard et al 2010;Vidal-Royo et al 2012;Durand-Riard et al 2013), 3D conformable mesh generation is a significant challenge at large scale. Mesh generation is often time-consuming and requires a very large number of elements to honor fine-scale structural features, especially in complex faulted contexts (Moretti 2008;Caumon 2010;Durand-Riard et al 2010;Botella et al 2013). Thus, in this work, we use simple geometric analysis to restore layers to their original depositional states applying surface restoration (also called 2.5D restoration).…”

Section: Restoration Goalsmentioning

confidence: 99%

Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials

et al. 2014

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International audienceThis work explains a procedure to predict Cu potentials in the ore-Kupferschiefer using structural surface-restoration and logistic regression (LR) analysis. The predictor in the assessments are established from the restored horizon that contains the ore-series. Applying flexural-slip to unfold/unfault the 3D model of the Fore-Sudetic Monocline, we obtained curvature for each restored time. We found that curvature represents one of the main structural features related to the Cu mineralization. Maximum curvature corresponds to high internal deformation in the restored layers, evidencing faulting and damaged areas in the 3D model. Thus, curvature may highlight fault systems that drove fluid circulation from the basement and host the early mineralization stages. In the Cu potential modeling, curvature, distance to the Fore-Sudetic Block and depth of restored Zechstein at Cretaceous time are used as predictors and proven Cu-potential areas as targets. Then, we applied LR analysis establishing the separating function between mineralized and non-mineralized locations. The LR models show positive correspondence between predicted probabilities of Cu-potentials and curvature estimated on the surface depicting the mineralized layer. Nevertheless, predicted probabilities are particularly higher using curvatures obtained from Late Paleozoic and Late Triassic restorations

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Towards Stochastic Time-Varying Geological Modeling

Cited by 65 publications

References 75 publications

Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

Uncertainty management in stratigraphic well correlation and stratigraphic architectures: A training-based method

Sampling the uncertainty associated with segmented normal fault interpretation using a stochastic downscaling method

Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials

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