An ensemble-based decision workflow for reservoir management

Abstract

It is challenging to make optimal field development and reservoir management decisions with diminishing resources and low-emission requirements. For an optimal exploitation of the reservoir fluids, it is necessary to introduce advanced digital tools and account for geological uncertainty when making decisions. This paper discusses an ensemble-based probabilistic decision-making workflow for closed-loop reservoir management. The reservoir model is a synthetic but realistic model with oil, gas, and water. We show how to use ensemble methods in a workflow for integrated uncertainty prediction, history matching, and robust optimization. The workflow uses advanced ensemble-based history-matching techniques to update a reservoir model in the annual maturation process. After the history-matching update, an optimization process decides the best wells to drill next to gain the highest net present value. An additional robust decision step evaluates the wells over the ensemble of reservoir models to ensure they lead to a positive ensemble-averaged net present value. The approach allows for up-to-date predictions, including uncertainty estimates, leading to improved decision support for field development, well-planning, production strategies, and reservoir management.