Steam Assisted Gravity Drainage (SAGD) has been widely applied to unlock hydrocarbon resources in oil sands reservoirs. This method uses steam, which is generated at the surface, to heat a formation and create a steam chamber around an injector. Past studies have indicated that reservoir heterogeneity is one of the crucial factors that directly affectthe performance of the SAGD process. This paper presents an innovative integrated modeling approach for evaluating, assisted history matching, and production forecasting of the SAGD process with the presence of a complex shale barrier system in oil sands reservoirs.
As SAGD is a strongly geological dependent recovery process and, unfortunately, there are many uncertainties associated with reservoir geology in reality. Therefore, it requires generating a large number of geological realizations to capture the critical effects of geology, especially with the presence of shale barriers, in history matching and field development planning of the SAGD process. To quantify the impact and improve the quality of history matching compared with the traditional method, an efficient integrated workflow has been developed in which geological information generated from a geological modeling package is automatically updated for a reservoir simulator and controlled by an intelligent optimizer in a big-loop modeling approach.
A detailed workflow on the integrated modeling approach that includes shale barriers for a typical oil sands reservoir is described in the first section of this paper. Shale bodies are geostatistically distributed in the geological models. A comprehensive parametric study was conducted with numerous geological realizations to identify the critical role of shale barriers in SAGD performance including shale geometry, shale length and thickness, shale distribution and proportions. Then the Bayesian algorithm with a Proxy-based Acceptance-Rejection sampling method is employed for assisted history matching of SAGD production profiles. With the presence of complex shale barriers, it requires simultaneous updating of both geological and reservoir engineering parameters. Using the proposed approach, the global history matching errors were drastically reduced in all production wells. Validation results indicate that the integrated modeling approach effectively helps to update the properties and distribution of shale barriers to find the closest geological distribution compared to the true solution. Finally, an ensemble of the best-matched simulation models is used to perform a probabilistic forecasting to capture the uncertainties in future production profiles.
Not limited to history matching ofthe SAGD process, the proposed approach can be also applied to different complex problems such as robust optimization for various recovery methods from conventional to unconventional reservoirs.