Reservoir modeling workflows have remained essentially unchanged for the past decade, facilitated by commercially available software packages such as Petrel and IRAP RMS. These workflows begin with the construction of a geo-cellular reservoir model, in which a largely deterministic structural and stratigraphic framework is used to define the overall reservoir volume, and also zones within the reservoir. A grid is then constructed within each zone, and geostatistical methods are used to populate each grid-cell with a geological indicator (such as facies or rock type) and associated petrophysical properties. The resulting models typically contain several millions to tens of millions of cells, and are typically upscaled onto a coarser grid prior to flow simulation. Despite its ubiquity, there are a number of shortcomings with this workflow, including (but not limited to):
- Conventional modeling workflows are slow, often requiring many months from the development of initial model concepts to flow simulation or other outputs.
- Conceptual geological models become fixed early in the modelling process, with uncertainty explored using geostatistical methods within the framework of a single conceptual model, rather than across a range of possible geological concepts.
- It is difficult or impossible to rapidly explore a range of conceptual models, and test how these might impact on reservoir behavior.
- Geostatistical modeling methods are often non-intuitive, and require inputs that are not closely linked to the underlying geological concept.
- Integration across different disciplines is made more difficult by the use of different software tools, and also by different model grid types and resolution (e.g. fine- versus coarse-grids for geological and flow simulation models; unstructured meshes for geomechanical modeling).