1887

Abstract

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.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601887
2016-08-29
2024-04-20
Loading full text...

Full text loading...

http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601887
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error