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.
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International audienceThis paper introduces a stochastic structural modelling method that honours interpretations of both faults and stratigraphic horizons on maps and cross-sections in conjunction with prior information, such as fault orientation and statistical size-displacement relationships. The generated stochastic models sample not only geometric uncertainty but also topological uncertainty about the fault network. Faults are simulated sequentially; at each step, fault traces are randomly chosen to constrain a fault surface in order to obtain consistent fault geometry and displacement profile. For each simulated fault network, stratigraphic modelling is performed to honour interpreted horizons using an implicit approach. Geometrical uncertainty on stratigraphic horizons can then be simulated by adding a correlated random noise to the stratigraphic scalar field. This strategy automatically maintains the continuity between faults and horizons. The method is applied to a Middle East field where stochastic structural models are generated from interpreted two-dimensional (2D) seismic lines, first by representing only stratigraphic uncertainty and then by adding uncertainty about the fault network. These two scenarios are compared in terms of gross rock volume (GRV) uncertainty and show a significant increase in GRV uncertainty when fault uncertainties are considered. This underlines the key role of faults in resource estimation uncertainties and advocates a more systematic fault uncertainty consideration in subsurface studies, especially in settings in which the data are sparse
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