Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods—Application to a West Africa Deepwater Turbidite Reservoir

Abstract

SummaryStochastic simulation allows generating multiple reservoir models that can be used to characterize reservoir uncertainty. In many practical situations, the large computation time needed for flow simulation does not allow an evaluation of flow on all reservoir models. In this paper, we propose a method to select a subset of reservoir models reflecting the same uncertainty in flow response as the full set. Using the concept of distance, we map the reservoir models to a low-dimensional space where kernel clustering is applied to identify a subset of representative reservoir models of the entire set. Flow simulation and subsequently uncertainty quantification are performed on this subset. A case study is presented of an architecturally complex deepwater turbidite offshore reservoir with large uncertainty in the type of depositional system present.