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Reservoir Modelling Using Parametric Surfaces and Dynamically Adaptive Fully Unstructured GridsNormal access

Authors: Y. Melnikova, C. Jacquemyn, H. Osman, P. Salinas, G. Gorman, G.J. Hampson and M.D. Jackson
Event name: ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery
Session: Geological Modelling
Publication date: 29 August 2016
DOI: 10.3997/2214-4609.201601887
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 4.25Mb )
Price: € 20

Geologic heterogeneities play a key role in reservoir performance. Surface based geologic modeling (SBGM) offers an alternative approach to conventional grid-based methods and allows multi-scale geologic features to be captured throughout the modeling process. In SBGM, all geologic features that impact the distribution of material properties, such as porosity and permeability, are modeled as a set of volumes bounded by surfaces. Within these volumes, the material properties are constant. The surfaces have parametric, grid-free representation, which, in principle, allows for unlimited complexity, since no resolution is implied at the stage of modeling and features of any scale can be included. Surface based models are discretized only when required for numerical analysis. We report here a new automated and integrated workflow for creating and meshing stochastic, surface-based models. Surfaces are represented through non-uniform rational B-splines (NURBS). Multiple relations between surfaces are captured through geologic rules that are translated into Boolean operations (intersection, union, subtraction). Finally, models are discretized using fully unstructured tetrahedral meshes coupled with a geometry-adaptive sizing function that efficiently approximate complex geometries. We demonstrate the new workflow via examples of multiple erosional channelized geobodies, fault models and a fracture network. We also show finite element flow simulations of the resulting geologic models, using the Imperial College Finite Element Reservoir Simulator (IC-FERST) that features dynamic adaptive mesh optimization. Mesh adaptivity allows us to focus computational effort on the areas of interest, such as the location of water saturation front. The new approach has broad application in modeling subsurface flow.

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