Summary Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to 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 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 evaluate the benefit of the use of closed-loop control strategies, on the basis of direct feedback between reservoir monitoring and inflow-valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimize a number of adjustable parameters within a general direct feedback relationship between measured data and inflow-control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal-well geometry and fluid contacts, and capture water and gas flow. Two inflow-control strategies are tested. The first is an open-loop approach, using fixed inflow-control devices to balance the pressure drawdown along the well, sized before installation. The second is a closed-loop, feedback-control strategy, using variable inflow-control valves that can be controlled from the surface in response to multiphase-flow data obtained downhole. The closed-loop strategy is optimized with a base-case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not reoptimizing. We find that closed-loop feedback control yields positive gains in net-present value (NPV) for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy. Closed-loop control also can yield positive gains in NPV even when the reservoir does not behave as expected, and in tested scenarios returned a near optimal NPV. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, because observed gains depend on when the calculation is made.

Declaration of Originality I declare that this thesis, Closed-loop Feedback Control of Intelligent Wells under Uncertainty, is entirely my own work under the supervision of Prof. Matthew D. Jackson. The work was performed in the Department of Earth Science and Engineering at Imperial College London. All published and unpublished material used in the thesis has been given full acknowledgment. This work has not been previously submitted, in whole or in part, to any other academic institution for a degree, diploma, or any other qualification.

Declaration of Originality I declare that this thesis, Closed-loop Feedback Control of Intelligent Wells under Uncertainty, is entirely my own work under the supervision of Prof. Matthew D. Jackson. The work was performed in the Department of Earth Science and Engineering at Imperial College London. All published and unpublished material used in the thesis has been given full acknowledgment. This work has not been previously submitted, in whole or in part, to any other academic institution for a degree, diploma, or any other qualification.

Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to 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 evaluate the benefit of using closed-loop control strategies, based on direct feedback between reservoir monitoring and inflow valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimise a number of adjustable parameters within a general direct feedback relationship between measured data and inflow control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal well geometry and fluid contacts, and capture water and gas flow. Two inflow control strategies are tested. The first is an open-loop approach, using fixed inflow control devices to balance the pressure drawdown along the well, sized prior to installation. The second is a closed-loop, feedback control strategy, employing variable inflow control valves that can be controlled from the surface in response to multiphase flow data obtained downhole. The closed-loop strategy is optimized using a base case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not re-optimising. We find that closed-loop feedback control yields positive gains in NPV for the majority of reservoir behaviours investigated, and higher gains than the open-loop strategy. Closed-loop control can also yield positive gains in NPV even when the reservoir does not behave as expected. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, as observed gains depend on when the calculation is made.

Optimized production strategies using intelligent wells have been shown in numerous studies to improve economic performance. However, most optimization methods are model-based, effective only if the reservoir model captures the range of all possible reservoir behaviors at the individual well and completion level. This is seldom the case. Furthermore, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. Motivated by this, recent studies have shown that direct feedback control, triggered by monitoring at the surface or downhole, can increase net-present-value (NPV) and mitigate reservoir uncertainty. This approach does not neglect model predictions entirely; rather, a model-based approach is used to optimize adjustable parameters in a generic feedback control algorithm. We evaluate the benefits of using direct feedback control for multi-well production optimization using the synthetic Brugge field case study. We test three inflow control strategies. Two are based on direct feedback control, but differ in the level of monitoring and control. In the first feedback control strategy, all monitoring and control is taken at surface, using surface multiphase flow meters and on/off well-head control valves. In the second, monitoring and control can take place either at surface or downhole, using on/off well-head and variable completion inflow control valves, in response to measurements from surface and downhole multiphase flow meters. These control strategies are optimized on a subset of the published model realizations; the other realizations are then used to simulate unexpected reservoir behavior. For benchmarking purposes, we implement a third, reactive rule based approach, heuristically developed with prior reservoir knowledge of the truth model. We also compare our results to previously published, model-based inflow control strategies developed by optimizing NPV with perfect knowledge of the Brugge truth case. Our results suggest that closed-loop direct feedback control, implemented at surface and downhole, can yield significantly higher NPV compared to surface feedback control alone. Moreover, despite the simplicity of the direct feedback control approach, the NPV returned is higher than a heuristic reactive approach, particularly when reservoir behavior is unexpected. In contrast to model-based optimization techniques, direct feedback control is straightforward to implement and can be easily applied in real field cases.