The Lower Cretaceous McMurray Formation in the Athabasca Oil Sands consists of channel belt deposits formed from meandering river systems. Large-scale fluvial point bars and other components of meander-belts compose this heterogeneous formation and are the source of complex sedimentary facies relationships. Recognition and correct interpretation of the spatial facies distribution, hence connectivity of the reservoir system, is essential to optimal field development and project economics. It is, therefore, crucial to understand river depositional processes, link associated facies to connectivity metrics, and implement them in flow modelling for hydrocarbon exploration.
In the geological modelling phase, we analyzed data collected through high-density drilling, extensive coring, and three-dimensional (3D) seismic to map the internal stratigraphic architecture for different reservoir levels. The model captures the 3D representation of different depositional elements, including point bars, counter point bars, side bars, and abandoned channel fills. The deterministic interpretations constrain the stochastic simulation of the reservoir parameters, and distinct morphology, facies associations, and reservoir potential characterize the zones. Our workflow improves the geological realism of subsurface models and allows quantitative analysis of the spatial uncertainty. Including depositional bedding geometries in the modelling helps reduce uncertainties in net continuous bitumen estimations. It improves the knowledge of reservoir connectivity and compartmentalization. The ultra-defined model provides the framework for detailed analysis and optimal field development.
This paper presents a new computationally efficient measure for connectivity based on detailed geological interpretations and mapping inclined heterolithic strata (IHS) in point bar deposits. In the calculations, we account for:
facies distributions, porosity, permeability along the principal flow axis, and oil saturation,
pressure and elevation (potential energy gradients),
well locations, and
tortuosity of the fluid flow streamlines.
To evaluate the effect of sedimentary heterogeneities on key reservoir performance indicators, we formulate the reservoir connectivity as a mathematical optimization problem and estimate the flux in the connected porosity.
Applying the methodology on a point-bar deposit shows that the connectivity factor strongly correlates with the ensuing recovery responses. This novel, computationally inexpensive approach captures the uncertainty in reservoir rock distributions and provides a quick and practical measurement for decision-making in reservoir management problems. Its features enable evaluating multiple reservoir parameters and using Monte Carlo techniques to quantify uncertainty and risk propagation in the presence of geological uncertainty to rank field portfolios. In the SAGD examples, the method estimates steam chamber development and conformance with high confidence, supporting optimal well placement for new development wells and infill drilling, optimizing the well spacing and orientation.