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

Nicholas Brown, Anna Roubickova, Ioanna Lampaki, Lucy MacGregor, Michelle Ellis, Paola Vera de Newton

Research output: Contribution to journalSpecial issuepeer-review

Abstract / Description of output

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 using supervised machine learning with a general aim of decreasing the petrophysical interpretation time down from over 7 days to 7 minutes. 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. Some popular machine learning framework are unable to take full advantage of modern HPC machines, and we explore our solutions. The result of this work is the ability, for the first time, to use machine learning for the entire petrophysical workflow.
Original languageEnglish
Article numbere5655
Number of pages19
JournalConcurrency and Computation: Practice and Experience
Issue number20
Early online date8 Jan 2020
Publication statusPublished - 25 Oct 2020

Keywords / Materials (for Non-textual outputs)

  • boosted trees
  • machine learning
  • neural networks
  • oil and gas
  • petrophysical interpretation


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