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A Hierarchical Marginal Marine Architectural Classification System Enables Nested Approach to Geocellular Modeling of Mixed-Process Reservoirs: An Example from the Horseshoe Canyon Formation, Alberta, Canada


New hierarchical concepts have been developed for the classification of process dominance and stratigraphic architecture in mixed-influence marginal marine systems. The new classification scheme is herein applied to intra-parasequence scale (one reservoir flow unit) geocellular modeling of the Upper Cretaceous (Campanian) Horseshoe Canyon Formation, Drumheller, Alberta, Canada. The Horseshoe Canyon succession represents a progradational sequence set composed of seven relatively thin (8 m) transgressive-regressive (T-R) sequences (A to G). Reservoir modeling is focused on a single T-R sequence (Unit C). Pseudo-3D outcrop exposure along the Red Deer Valley and Willow Creek allows for mapping the lateral extent and the process character of architectural units. Four cored wells and seventy five wireline logs allow the extrapolation of the mapped units into the subsurface. This provides an excellent data set for testing the application of the hierarchical classification system to 3D geocellular modeling. A nested hierarchy of architectural units is used in the reservoir modeling process. It includes, at the largest scale, the T-R Sequence, composed of a Transgressive and Regressive Element Complex Assemblage Set (TECAS and RECAS). At the next level Element Complex Assemblages (ECA) comprise Element Complexes (EC) deposited under similar process conditions. At the smallest scale Element Sets (ES) and Elements (E) are considered the basic building blocks of the depositional system. The top level architectural units contain the smaller levels nested within them, modeled using zones, sub-zones and discrete facies elements. This nested architectural classification enables identification of parent, child and sibling relationships between the different hierarchies, facilitating organized collation of geometric and relational data, as well as quantitative prediction of the distribution and geometry of geobodies in unsampled areas. The resultant reservoir analogue models enhance our understanding of the 3D geometry of the studied depositional system and can be used to investigate the impact of intra-parasequence scale architectures on reservoir connectivity and performance. Importantly, the predictive power of the architectural classification scheme reduces uncertainties related to stochastic modeling methods, creating models that are arguably more geologically realistic and more effective when applied to reservoir uncertainty management and production decisions.