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

Using machine learning to reduce ensembles of geological models for oil and gas exploration