Complex Geological Modeling and Quality Assurance Using Unstructured Grids

Abstract Conceptual limitations of existing gridding technologies often lead to undesirable simplifications to the modeling of structurally complex areas, and consequently poor predictions. We present a structural modeling and gridding workflow that limits these modeling compromises. A volume-based 3D structural model based on fault and horizon surfaces is constructed from input data that has undergone basic quality checking using a variety of techniques. The critical step in the grid creation is the definition of a flattened (‘depositional’) space that deforms the structural model mesh under mechanical constraints. A 3D ‘unstructured’ grid is created in the depositional space, based on ‘cutting’ a property-populated, regular cuboidal grid by the geological discontinuities. The tectonic consistency and better preservation of geodetic distance make the flattened space ideal for a range of property modeling approaches. The forward-deformation of the grid into true geological space tends to preserve the layer-orthogonality of the grid columns and makes the grid more suited to numerical simulation approximations. The final grid is unstructured, high quality and an accurate representation of the input structural model. The 3D structural model, depositional space transform and grid geometries all provide valuable information on the structural quality of the input data. The stretching and deforming of the orthogonal local axes in the transformation from depositional space to geological space are used to focus further effort on structural model quality assurance (QA). The key step in generating accurate property population and simulation models is the application of QA metrics on the grid geometry; the transformation from depositional space to geological space is used to generate a set of grid properties that highlight potential structural inconsistencies or data quality issues back in the structural model. We present several examples based on a range of structurally complex models, and demonstrate the downstream impact of applying this QA workflow throughout the stages of input data validation, structural model creation and grid creation.