  Exploration using borehole drilling is a key activity in determining the most
appropriate locations for the petroleum industry to develop oil fields.
However, estimating the amount of Oil In Place (OIP) relies on computing with a
very significant number of geological models, which, due to the ever increasing
capability to capture and refine data, is becoming infeasible. As such, data
reduction techniques are required to reduce this set down to a smaller, yet
still fully representative ensemble. In this paper we explore different
approaches to identifying the key grouping of models, based on their most
important features, and then using this information select a reduced set which
we can be confident fully represent the overall model space. The result of this
work is an approach which enables us to describe the entire state space using
only 0.5\% of the models, along with a series of lessons learnt. The techniques
that we describe are not only applicable to oil and gas exploration, but also
more generally to the HPC community as we are forced to work with reduced
data-sets due to the rapid increase in data collection capability.
