Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration

• URL: http://arxiv.org/abs/2010.02087v1
• Date: Thu, 1 Oct 2020 10:14:58 GMT
• Title: Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration
• Authors: Nick Brown, Anna Roubickova, Ioanna Lampaki, Lucy MacGregor, Michelle Ellis, Paola Vera de Newton
• Abstract summary: In this paper we present work done, in collaboration with Rock Solid Images (RSI), using supervised machine learning on a Cray XC30. We describe the use of mathematical models that have been trained using raw well log data. We explore how the predictions from these models compare against the interpretations of human petrophysicists.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The oil and gas industry is awash with sub-surface data, which is used to characterize the rock and fluid properties beneath the seabed. This in turn drives commercial decision making and exploration, but the industry currently relies upon highly manual workflows when processing data. A key question is whether this can be improved using machine learning to complement the activities of petrophysicists searching for hydrocarbons. In this paper we present work done, in collaboration with Rock Solid Images (RSI), using supervised machine learning on a Cray XC30 to train models that streamline the manual data interpretation process. With a general aim of decreasing the petrophysical interpretation time down from over 7 days to 7 minutes, in this paper we describe the use of mathematical models that have been trained using raw well log data, for completing each of the four stages of a petrophysical interpretation workflow, along with initial data cleaning. We explore how the predictions from these models compare against the interpretations of human petrophysicists, along with numerous options and techniques that were used to optimise the prediction of our models. The power provided by modern supercomputers such as Cray machines is crucial here, but some popular machine learning framework are unable to take full advantage of modern HPC machines. As such we will also explore the suitability of the machine learning tools we have used, and describe steps we took to work round their limitations. The result of this work is the ability, for the first time, to use machine learning for the entire petrophysical workflow. Whilst there are numerous challenges, limitations and caveats, we demonstrate that machine learning has an important role to play in the processing of sub-surface data.

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