Uncertainty is an integral part of risk and decision making. Uncertainty in the reservoir is caused by a lack of knowledge on key geologic and reservoir engineering factors that are required for building geologic and flow models. Traditional approaches to modeling uncertainty, such as those relying on the experimental design technique and Monte Carlo simulation, are either limited in effectiveness (unable to handle general cases) or efficiency (too demanding on the central processing unit). We review a new technique for modeling reservoir performance uncertainty that is more general and efficient. The technique relies on the definition of a distance between any two reservoir models. This distance should correlate with the difference in reservoir response and provides the key missing link between geologic uncertainty and flow uncertainty. We present a workflow combining several statistical tools such as multidimensional scaling and clustering to model reservoir response uncertainty when both geologic and engineering parameters are uncertain. At the same time, this workflow allows assessment of the most influential parameters regarding flow, as well as accuracy of the uncertainty assessment. Two real field cases illustrate this approach.