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Fostering high-impact machine learning ecosystem in subsurface science and engineeringNormal access

Author: M. Hall
Event name: 79th EAGE Conference and Exhibition 2017 - Workshops
Session: WS01 Data Science for Geosciences
Publication date: 12 June 2017
DOI: 10.3997/2214-4609.201701652
Organisations: EAGE
Language: English
Info: Abstract, PDF ( 341.85Kb )
Price: € 20

Summary:
The field of machine learning is experiencing a boom. The International Energy Agency predicts the 2020 analytics market in upstream petroleum alone will more than double to $10 billion. Previous such hype cycles, especially the one at the end of the 1980s, ended in a mass extinction event: artificial intelligence companies died off, funding seas dried up, and 'expert systems' became dirty words. Meanwhile, however, research continued under codenames like 'informatics', 'machine learning', 'big data', and 'data analytics'. Today, as the AI spring gives way to an AI summer, how can we give our projects the best chance of having the impact we believe they can have? As the petroleum industry moves into its autumnal years, I propose eight strategies for the computational science and engineering community to bring about the profound changes to its safety and operating efficiency that we all believe we can achieve. These strategies are well tested in other fields, and many of them have at least been tried in subsurface science and engineering.


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