Hydrocarbon reservoirs typically contain an array of complex geological heterogeneities that are at, or below, the resolution of seismic data, so their geometry and spatial distribution is uncertain. These heterogeneities may be structural, stratigraphic, sedimentologic and/or diagenetic on origin, and often impact flow behavior and hydrocarbon recovery; hence, they must be captured in reservoir models. 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).
This project will address these shortcomings by developing rapid reservoir modeling (RRM) software for prototyping complex reservoir models, by means of novel interactive modeling techniques, exploratory visualization, and numerical analysis. The new software will not replace existing workflows; rather, it will supplement them by allowing the rapid testing of geologic concepts and how these impact on reservoir behavior. RRM will also facilitate the generation of reservoir geometries and meshes for consistent input to seismic, geomodel, geomechanical and flow simulation models, and the preservation of quantitative, 3D conceptual models for use in other studies that can be easily modified. The software is differentiated from other products by the rapid and intuitive generation of reservoir geometries using sketch-based interfaces and modeling (SBIM).
The project leverages existing workflows and software packages, by integrating unique expertise at four world-leading research groups at the academic partner institutions. It is also underpinned by existing (joint) research activities carried out by a large body of PhD and MSc students and postdoctoral researchers. The final product will be based on software developed for 10+ years at the academic partners, but have interfaces to standard commercial reservoir modeling and simulation packages at any stage of the RRM workflow, allowing continuous interchange of data and models.