The efficiency of steam assisted gravity drainage (SAGD) operation depends on developing a uniform steam chamber by maintaining an optimal subcool temperature along the length of the well pair. Implementing operational parameters obtained from model‐based optimization directly in the field may not lead to the desired subcool temperature. Based on the real‐time measurements from surface and downhole sensors, along with other well and surface constraint information, a real‐time feedback control of SAGD well pairs can be implemented to optimize subcool and steam chamber development. Model predictive control (MPC), which is a multivariable constrained controller, provides a framework for such control. To evaluate the use of MPC for real‐time control of SAGD wells, a case study is performed using a 3D heterogeneous reservoir model. Porosity and permeability realizations are created and ranked based on net present value (NPV). One of the realizations is considered as the ‘true’ reservoir, and two other realizations are selected to represent different cases of uncertain reservoir models. For each model, a reservoir simulator is used to find the optimum rates and subcool temperature, which become the set‐points for MPC to operate the wells. We compare the steam chamber growth and NPV calculated using MPC with the base case where no MPC is used and discuss the implementation advantages. Since MPC led to expedited and accurate tracking of optimized operating targets obtained using reservoir models, ∼18 % improvement in NPV is achieved compared to manual open‐loop application of optimum rates, a practice used commonly in the field.

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