Integrated dynamic optimization and control in reservoir engineering using locally identified linear models

Studies on dynamic real-time optimization (D-RTO) of waterflooding strategies in petroleum reservoirs have demonstrated that there exists a large potential to improve economic performance in oil recovery. Unfortunately, the used large-scale, nonlinear, physics-based reservoir models suffer from vast parametric uncertainty and generally poor short-term predictions. This seriously limits the industrial and economic feasibility of a production strategy based at long-term economic objectives alone. In this work, a two level optimization and control strategy is investigated, consisting of a D-RTO and model predictive control (MPC) level. In this structure, long-term economic performance is addressed by the design of optimal reference trajectories, while reliability of reaching these long-term goals is managed through a short-term tracking control problem, based on locally identified linear models.