An ensemble-based decision workflow for reservoir management

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