The vast majority of grids for reservoir modeling and simulation workflows are based on pillar gridding or stairstep grid technologies. The grids are part of a feature-rich and well-established modeling workflow provided by many commercial software packages. Undesirable and significant simplifications to the gridding often arise when employing such approaches in structurally complex areas, and this will clearly lead to poor predictions from the downstream modeling.
In the classical gridding and modeling workflow, the grid is built in geological space from input horizon and fault interpretations, and the property modeling occurs in an approximated ‘depositional’ space generated from the geological space grid cells. The unstructured grids that we consider here are based on a very different workflow: a volume-based structural model is first constructed from the fault/horizon input data; a flattening (‘depositional’) mapping deforms the mesh of the structural model under mechanical and geometric constraints; the property modeling occurs in this depositional space on a regular cuboidal grid; after ‘cutting’ this grid by the geological discontinuities, the inverse depositional mapping recovers the final unstructured grid in geological space. A critical part of the depositional transformation is the improved preservation of geodetic distances and the layer-orthogonality of the grid cells.
The final grid is an accurate representation of the input structural model, and therefore the quality checking of the modeling workflow must be focused on the input data and structural model creation. We describe a variety of basic quality checking and structurally-focused tools that should be applied at this stage; these tools aim to ensure the accuracy of the depositional transformation, and consequently ensure both the quality of the generated grid and the consistent representation of the property models. A variety of quality assurance metrics applied to the depositional/geological grid geometries provide spatial measures of the ‘quality’ of the gridding and modeling workflow, and the ultimate validation of the structural quality of the input data.
Two case studies will be used to demonstrate this novel workflow for creating high-quality unstructured grids in structurally complex areas. The improved quality is validated by monitoring downstream impacts on property prediction and reservoir simulation; these improved prediction scenarios are a more accurate basis for history matching approaches.