Summary
We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level, these profiles are considered as set points (reference values) for a tracking control algorithm, also known as a model predictive controller (MPC), to optimize the production variables over a short moving horizon on the basis of a simple data-driven model. In the process industry such a two-level approach is a well-known strategy to correct for small local disturbances that may have a negative (cumulative) effect on the long-term production strategy. We used a conventional reservoir simulator with gradient-based optimization functionality to perform the life-cycle optimization. Next, we applied this long-term strategy to a reservoir model, representing the truth, with somewhat different geological characteristics and near-wellbore characteristics not captured in the reservoir model used for the longterm optimization. We compared the performance (oil recovery) of this truth model when applying the life-cycle strategy with and without the corrections provided by the data-driven algorithm and the tracking controller. In this theoretical study we observed that the use of the lower-level controller enabled successful tracking of the reference values provided by the upper-level optimizer. In our example, a performance drop of 6.4% in net present value (NPV), caused by differences between the reservoir model used for life-cycle optimization and the true reservoir, was successfully reduced to only 0.5% when applying the two-level strategy. Several studies have demonstrated that model-based life-cycle production optimization has a large scope to improve long-term economic performance of waterflooding projects. However, because of uncertainties in geology, economics, and operational decisions, such life-cycle strategies cannot simply be applied in reality. Our two-level approach offers a potential solution to realize life-cycle optimization in an operational setting.
Integration of expert and data-driven workflows to manage reservoir and well life cycle in Arctic conditions using innovative SICLO methodology
