When assessing a reservoir’s Hydrocarbon Initially In Place (HIIP) volumes there are often many uncertain input factors. If they are discretized into low, mid and high case scenarios, this can result in tens of thousands of possible combinations of factors and it is often necessary to reduce the amount of uncertain factors to a manageable number. This can reduce the range of model outcomes, and the confidence in the predicted volumes will be overestimated. Experimental design allows a reduced representative set of models to be constructed for uncertainty analysis when it would take too long to construct all possible realizations. This paper uses a case study to demonstrate a refined application of experimental design, whereby uncertain factors were first identified and experts were consulted to specify high, mid and low values with associated probabilities. The analysis was completed in two parts: a screening phase, whereby non-significant factors were eliminated; and an optimization phase, whereby a limited number of model runs were used to train a proxy model for use in a Monte Carlo procedure to generate a probability distribution of HIIP. The key advantage of the method is the comprehensive treatment of uncertainty with a reduced amount of modelling effort.