A Proxy Model for Predicting SAGD Production from Reservoirs Containing Shale Barriers

Abstract

AbstractAn approach based on artificial intelligence (AI) tools is being used to explore the influence of shale heterogeneities on SAGD production. In this project, the production data is derived from a set of synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints representative of Athabasca oil sands reservoirs.The underlying reservoir simulation model is homogeneous, and two-dimensional. Its petrophysical properties, such as the porosity, permeability, initial oil saturation and net pay thickness, have been taken from average values for several pads in Suncor's Firebag project. Superimposed on this homogeneous reservoir model are sets of idealized shale barrier configurations. The permeability of each shale barrier is several orders of magnitude smaller than the permeability of the oil sand in the simulation model. The individual shale barriers are categorized by their location relative to the SAGD well pair (vertical and lateral), and by their geometry (thickness and lateral extent).SAGD production was simulated with the reservoir model for a training set of shale barrier configurations. The training set was chosen to try to span the space of possible shale barrier configurations in the reservoir model; the elements of the set were identified by means of experimental design. A network model based on AI tools was constructed to match the output of the reservoir simulation model for this training set of shale barrier configurations, with a focus on the production rate and the steam-oil ratio (SOR). Then the trained AI proxy model was used to predict SAGD production profiles for scenarios where the shale barriers were distributed stochastically. The results of these predictions were compared with the results of the SAGD simulation model with the same shale barrier configurations. The match to production rate and SOR was good, better than expected.The results of this work demonstrate the capability and flexibility of the AI-based network model, and of the parameterization technique for representing the characteristics of the shale barriers, in capturing the effects of complex heterogeneities on SAGD production. The approach could be extended to study the effects of other heterogeneous features such as lean zones on SAGD production. It offers the significant potential of providing an indirect method for inferring the presence and distribution of heterogeneous reservoir features from the profiles of SAGD field production data.