Significant challenges 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 models are rarely predictive at the spatial and temporal scales required to identify control actions. We suggest that 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 assess the implementation of an intelligent horizontal well in a thin oil reservoir in the presence of reservoir uncertainty, and evaluate the benefit of using different inflow control actuators in conjunction with surface and/or downhole monitoring equipment.

Four inflow control strategies are tested. The first is an open-loop approach, using a fixed control device to balance inflow along the well, sized prior to installation. The second and third are closed-loop feedback control strategies, employing intelligent completions that can be controlled from the surface. The second strategy uses on/off inflow control devices, operated according to surface flow rates and phase measurements obtained during zonal well tests. The third strategy uses variable control devices, operated according to downhole multiphase flow meters. The fourth strategy employs a gradient-based optimization algorithm to find the dynamic optimal inflow control behavior. This strategy assumes perfect reservoir knowledge and is implemented only for benchmarking of the feedback control strategies.

Our result suggests that closed-loop control based on direct feedback between reservoir monitoring and inflow valve settings can yield close-to-optimal gains in NPV compared to uncontrolled production, even if the reservoir does not behave as predicted. Open-loop control yields significantly lower gains in NPV and is also a riskier strategy, because unpredicted reservoir behavior can lead to negative returns. Closed-loop feedback control can mitigate uncertain reservoir behavior, even when this lies outside the range of model predictions; moreover, the uncertainties which have the most significant impact on production may not be the most difficult to mitigate.

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