Abstract

An artificial intelligence (AI) based workflow is being deployed to develop and test procedures for estimating shale barrier configurations from SAGD production profiles. The data employed in this project is derived from a set of synthetic SAGD reservoir simulations based on petrophysical properties and operational constraints representative of Athabasca oil sands reservoirs.

Initially, a two-dimensional reservoir simulation model is being employed. The underlying model is homogeneous. 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. Reservoir heterogeneities are simulated by superimposing sets of idealized shale barrier configurations on the homogeneous model. The location and geometry of each shale barrier is parameterized by a unique set of indices. The resulting heterogeneous model is subjected to flow simulation to simulate SAGD production.

Next, a two-step workflow is followed: (1) a network model based on AI tools is constructed to match the output of the reservoir simulation (shale indices are inputs, while production rate and the steam-oil ratio are outputs) for a known training set of shale barrier configurations; (2) for a new SAGD production history generated via reservoir simulation with a shale barrier configuration that is unknown to the AI model generated in step 1, an optimization scheme based on a genetic algorithm approach is adopted to perturb the shale indices until the difference between the target production history and the production history predicted from the AI model is minimized.

A number of cases have been tested. The results show a good agreement between the shale barrier configurations predicted by the AI model with the configurations used to generate production histories in the reservoir simulation model (i.e., the "true" model). Thus, this optimization workflow offers potential to become an alternative tool for indirect inference of the uncertain distribution of shale barriers in SAGD reservoirs from data capturing field performance.

This works highlights the potential of an AI-based workflow to infer the presence and distribution of heterogeneous shale barriers from field SAGD production time-series data. It presents an innovative parameterization scheme suitable for representing heterogeneous characteristics of shale barriers. If this approach proves to be successful, it could allow the distribution of shale barriers to be inferred together with the impact of these barriers on SAGD performance. This would provide a basis for developing operating strategies to reduce the impact of the barriers.

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