Real‐time feedback control of SAGD wells using model predictive control to optimize steam chamber development under uncertainty

Abstract

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.

Dynamic modelling and predictive control for the sequential collaborative reactors of cobalt removal process under time‐varying conditions

“…Since the actual cobalt removal process has both main reactions and side reactions (the interaction mechanism between them is largely unknown), and the time‐varying inlet and reaction conditions introduce fluctuations to the process dynamics, it is difficult to build a system model with fixed parameters that is capable of describing the global process dynamics for its whole operation range. However, model‐based optimal control approaches (such as internal model control, model reference adaptive control, model predictive control, and tracking control) closely rely on an accurate and applicable process model. Therefore, it is essential to develop an appropriate dynamic model to describe the relationship of these sequential, collaborative reactors under time‐varying conditions.…”

Real‐time multivariable model predictive control for steam‐assisted gravity drainage

“…Moreover, this subcool temperature difference is the key to maintaining the height of the liquid pool at a certain desired level . Research on steam trap or subcool control via proportional integral derivative (PID) control, and more advanced methods like model predictive control (MPC), have proven to be an improvement over open loop methods of steam injection for oil recovery. However, PID controllers are incapable of handling constraints, calculating optimal control gains for the manipulated variable (MV), and are incapable of Multi‐input multioutput (MIMO) control.…”

“…The other major drawback of steam trap control for both PID and MPC control is the absence of a set procedure to determine the optimal subcool or liquid pool temperature ( T P ) setpoint for a given formation. Also, assuming an average temperature for the liquid pool may not be suitable since different points along the well are at different temperatures and production of live steam may not be captured. Another challenge arises from the changing system boundary as the steam chamber grows and the absence of a technique to produce the liquid accumulating in the liquid pool.…”

“…The manipulated variables considered in this work are liquid production rate ( LR ) and injection steam rate ( ISR ). While the use of MPC for oil recovery is not new, the use of a MIMO MPC for SAGD to control OR and the optimal has not been explored before. The real‐time implementation of the control strategies was made possible by establishing a bidirectional communication link between the computer modeling group ( CMG ) STARS TM and MATLAB software.…”

Issue Highlights

“…A case study conducted in this research demonstrates the improvement in cumulative steam oil ratio and overall SAGD economics due to effi cient steam utilization and optimal operating strategy. [2] 1382 …”