Self-Learning Reservoir Management

In this work, we present an industrial automation framework for control and optimization of hydrocarbon producing fields while satisfying business and physical constraints. The all-encompassing reservoir management problem is decomposed into a series of different-time-scales optimization exercises. A model predictive control strategy is used to regulate well and field instrumentation. The optimum field operating point, which changes with time, satisfies the maximum economic return. The work is a starting point for further development in automatic intelligent reservoir technologies.