Reservoir uncertainty-tolerant, proactive control of intelligent wells

Intelligent wells (I-wells) can provide a layer-by-layer production and injection control. The flow control flexibility relies on the real-time operation of multiple, downhole Interval Control Valves (ICVs) installed across the well completion intervals. Proactive control of I-wells, with its ambition of creating an optimal, operational strategy of ICVs for the full well lifetime, is a computationally demanding optimisation problem. Reservoir uncertainty adds an additional level of complexity related to the reliability of the reservoir model's prediction. This, along with the large number of control variables involved in the proactive I-well control, make the traditional meta-heuristic optimisation approaches (e.g. Genetic Algorithms (GA)) inefficient. A stochastic search algorithm based on the Simultaneous Perturbation Stochastic Approximation (SPSA) is employed to solve the proactive, I-well control problem while a utility function approach is used to define the objective function to allow for the uncertainty in the reservoir's description. The utility function accounts for both the expectation and variance of the Net Present Value (NPV) function while using the same control for the different reservoir model realisations. This approach to identifying a robust control strategy is an improvement compared with the traditional methods that rely on either a single realisation or on the mathematical expectation of the objective function. The proposed robust optimisation framework is compared with the traditional methods on an uncertain reservoir model. It is found that the optimal control provided by ICVs inherently reduces the variance during the mean optimisation approach. The initial stages of optimisation when the control variables were far from the optimum value did not show conflicting behaviour when attempting to increase the mean and reduce the variance individually. However, conflict was observed between these two objectives during the later stages of optimisation as the optimum value of the augmented objective function is approached. A utility function approach is shown to be an efficient procedure for identifying the control scenario with the maximum expectation of added-value at an adjustable level of risk.