|Abstract: ||Important challenges still remain in the development of optimized control techniques for intelligent wells, particularly with respect to properly incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization capture all possible reservoir behaviors at the individual well and completion level. This is rarely the case. Moreover, reservoir simulation models are rarely predictive at the spatial and temporal scales required to identify control actions. We suggest that simple, closed-loop feedback control strategies, triggered by monitoring at the surface or downhole, can increase NPV and mitigate reservoir uncertainty. We do not neglect reservoir model predictions entirely; rather, we use a model-based approach to optimize adjustable parameters in the feedback control strategies.
We evaluate the benefits of closed-loop feedback control using downhole and/or surface monitoring sensors and inflow control valves (ICVs), in comparison to uncontrolled (open-hole) production, open-loop inflow control using fixed control devices (FCDs) sized prior installation, and a heuristic reactive inflow control approach using surface and downhole monitoring and ICVs. For benchmarking purposes, we also compare our feedback control approaches against the optimal dynamic solution found using model-based control and assuming a perfectly predictive model is available. The benefits of closed-loop feedback control are evaluated for three different reservoir and production scenarios. The first scenario is a synthetic thin oil-rim reservoir producing via aquifer influx using a single long horizontal well. The second is a high-resolution sector model from Troll West, which hosts a thin oil-rim, overlain by a large gas cap and underlain by an active aquifer, with oil produced via a single long horizontal well. The third reservoir production scenario is the SPE Brugge field model, a reference model for comparing history matching and production optimization strategies. The Brugge field is a synthetic example of a geologically complex oil reservoir, produced by water flooding using 10 vertical injector wells and 20 vertical producer wells.
In all the scenarios investigated, we find that our closed-loop control algorithm, based on direct feedback between reservoir monitoring and inflow valve settings, yields close-to-optimal gains in NPV compared to uncontrolled production. Moreover, despite the simplicity of the direct feedback control approach, the NPV returned is higher than open-loop or heuristic control approaches, particularly when reservoir behavior is unexpected. In contrast to model-based optimization techniques, our direct feedback control approach is straightforward to implement and can be easily applied in real field cases.|