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

TL;DR

This paper presents a data reduction method that efficiently condenses large ensembles of geological models into a small, representative subset, significantly aiding oil exploration and potentially benefiting high-performance computing applications.

Contribution

The paper introduces a novel approach for selecting a minimal yet representative subset of geological models based on key features, reducing the ensemble size to 0.5% while maintaining full coverage.

Findings

Reduced model ensemble to 0.5% of original size

Effective identification of key features for model grouping

Applicable to both oil exploration and high-performance computing

Abstract

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.