Optimal model-based reservoir management with model parameter uncertainty updates

The objective of this work is to manage water flooding of a reservoir to achieve optimal oil production by employing an optimal model-based control framework that uses uncertain parameter updating and a particular reduced-order model. A Markov chain Monte Carlo method is used to update the proposed distributions of the uncertain parameters. To avoid excessive simulations of the complex reservoir model, the techniques of partial least square regression and the Karhunen-Loève expansion are used to find the relationships between the uncertain parameters and the system state. To demonstrate this approach, the optimal control of an oil producing reservoir is compared against an uncontrolled reservoir.