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2018 AAPG International Conference and Exhibition

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Complex Geological Modeling and Quality Assurance Using Unstructured Grids


In a new approach to creating complex and accurate 3D models in structurally complex areas, a volume-based 3D structural model is transformed under geomechanical constraints to a depositional space, where the gridding and property population occur. Of particular importance are data and model quality checks, and using grid properties derived from the geomechanical flattening to highlight and resolve modeling quality issues. Conceptual limitations of existing gridding technologies often lead to undesirable simplifications to the modeling of structurally complex areas and, consequently, poor predictions. We developed a structural modeling and gridding workflow that limits these modeling compromises. A 3D structural model based on fault and horizon surfaces is constructed from input data that has undergone basic quality checking. The critical step in the grid creation is the definition of a depositional space that deforms the structural model mesh under geomechanical constraints. A 3D ‘unstructured’ grid is created in the depositional space, based on ‘cutting’ a property-populated, regular cuboidal grid by the faults and unconformities. The tectonic consistency and preservation of geodetic distance that are inherent to the depositional transform make the flattened space ideal for a range of property modeling approaches. The forward-deformation of the grid into true geological space preserves the layer-orthogonality of the grid columns and makes the grid well suited to numerical simulation approximations. The modeling stages all provide important information on the structural quality of the input data. The stretching and deforming of the orthogonal local axes in the transformation from depositional to geological space are used to focus further effort on quality assurance (QA). The key step in generating accurate property population and simulation models is the application of QA tools on the grid geometry; the transformation from depositional to geological space is used to generate a set of grid properties that highlight potential structural inconsistencies or data quality issues in the structural model. We applied the workflow to several case studies in structurally complex areas. The quality of the grid is ensured through a range of QA metrics at various stages of the workflow, from input data to grid creation. The improved quality is validated by monitoring downstream impacts on property population and reservoir simulation.