Stochastic simulations of fault networks in 3D structural modeling

Abstract

Abstract3D Structural modeling is a major instrument in geosciences, e.g. for the assessment of groundwater and energy resources or nuclear waste underground storage. Fault network modeling is a particularly crucial step during this task, for faults compartmentalize rock units and play a key role in subsurface flow, whether faults are sealing barriers or drains.Whereas most structural uncertainty modeling techniques only allow for geometrical changes and keep the topology fixed, we propose a new method for creating realistic stochastic fault networks with different topologies. The idea is to combine an implicit representation of geological surfaces which provides new perspectives for handling topological changes with a stochastic binary tree to represent the spatial regions. Each node of the tree is a fault, separating the space in two fault blocks. Changes in this binary tree modify the fault relations and therefore the topology of the model. RésuméSimulations stochastiques de réseaux de failles en modélisation structurale 3D. La modélisation structurale est largement utilisée en géoscience, notamment pour l'évaluation des ressourcesénergétiques et hydriques du sous-sol. La caractérisation des failles est l'une desétapes clés du processus de modélisationétant donné leur importance dans lesécoulements de subsurface.Alors que la plupart des techniques de modélisation d'incertitudes structurales existantes perturbent seulement la géométrie des objets, nous proposons une nouvelle méthode de simulation stochastique de réseaux de failles, incluant des changements topologiques. Cette méthode associe une modélisation implicite des surfaces géologiques, avec un arbre binaire permettant d'agencer les régions spatiales du modèle. Chaque noeud de l'arbre représente une faille, séparant l'espace en deux blocs. Des changements dans l'arbre binaire modifient les relations entre failles et par conséquent la topologie du modèle.

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

“…The geometry of these "data" and of the associated fault locations is well constrained close to boreholes but is obviously more uncertain in sparsely drilled areas. Structural uncertainty assessment methods (Wellmann et al 2010;Cherpeau et al 2010) could be used to address these sources of uncertainty. In the present study, we consider that bias due to possible interpretation errors may be present in areas with low borehole density, especially in the NE and SE parts of the study area (Fig.…”

“…If we introduce an inferred fault that does not corresponds to the geologic record then the results will be biased. Wrong modeled faults can be the product of bad interpretation, missing data or noise in the raw data (Cherpeau et al 2010). Instead, it is possible to represent horizons as smoothed continuous surfaces and then use geometric attributes to detect fault activity (see previous section) (Fig.…”

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

“…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.…”

A 3D modeling approach to complex faults with multi-source data

“…The model may be conditioned using seismic and well data, while still allowing efficient simulation of realizations. Some structural uncertainty modeling techniques either allow for geometrical changes and keep the topology fixed, or create realistic stochastic fault networks with different topologies where the number and features of faults are changeable by taking into account fault-related uncertainties induced by subsurface imaging and interpretation ambiguities (Cherpeau et al, 2010;Cherpeau and Caumon, 2012).…”

“…By using a combined cognitive and geostatistical approach, the resultant 3D model is more consistent with current geological observation and understanding (Royse, 2009). A 3D parametric fault representation has been proposed for modeling the displacement field associated with faults in accordance with their geometry (Cherpeau et al, 2010;Laurent et al, 2013). Holden et al (2003) describe a stochastic model for the reservoir structure that may be used to represent the uncertainty of the structural model.…”