Closed-Loop Feedback Control for Production Optimization of Intelligent Wells Under Uncertainty

Abstract

Summary A key component of intelligent-well systems is the decision framework used to identify control actions for production optimization. Most published algorithms use model-based control, yet model-based techniques are effective only if the model or ensemble of models used in the optimization captures 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 show that simple, closed-loop feedback control, triggered by monitoring at the surface or downhole, can increase the net present value (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 feedback-control strategies. We compare open-loop control, by use of fixed control devices (FCDs), with closed-loop feedback control, by use of "on/off" inflow-control valves (ICVs), operated in response to monitoring at the wellhead, and "variable" ICVs operated in response to monitoring downhole. We also use 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, on the basis of direct feedback between reservoir monitoring and inflow-valve settings, can yield close-to-optimal gains in NPV compared with uncontrolled production, even if the reservoir behavior lies outside the range predicted by reservoir models. Moreover, similar gains are observed by use of surface monitoring and simple on/off ICVs, and downhole monitoring and variable ICVs. In contrast, open-loop control yields significantly lower NPV gains, and is also a riskier strategy because unpredicted reservoir behavior can yield suboptimal sizing of the FCDs and negative returns.