Machine learning on Crays to optimize petrophysical workflows in oil and gas exploration. (8th January 2020)

• Record Type: Journal Article
• Authors: Brown, Nick; Roubíčková, Anna; Lampaki, Ioanna; MacGregor, Lucy; Ellis, Michelle; Vera de Newton, Paola
• Other Names: Gil‐Costa Veronica guestEditor.; Senger Hermes guestEditor.; Robinson Timothy W. guestEditor.; Thota Abhinav guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 20(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-08
• Subjects: boosted trees -- machine learning -- neural networks -- oil and gas -- petrophysical interpretation; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5655
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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 drives commercial decision making and exploration, but the industry relies upon highly manual workflows when processing data. A question is whether this can be improved using machine learning, complementing the activities of petrophysicists searching for hydrocarbons. In this paper, we present work using supervised learning with the 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, to complete each of the four stages of a petrophysical interpretation workflow, in addition to initial data cleaning. We explore how the predictions from these models compare against the interpretations of human petrophysicists, and numerous options and techniques that were used to optimize the models. The result of this work is the ability, for the first time, to use machine learning for the entire petrophysical workflow.