Webinar: Summary and results from the Force 2020 Machine Learning Contest

Authors

Peter Bormann, Principal Geologist- ConocoPhillips
Peter.bormann@cop.com

Peder Aursand, Senior Engineer – Machine Learning- AkerBP
peder.aursand@akerbp.com

Abstract

The current revolution in data science / machine learning is fueled by open data sets, a democratization of tools and global real time access of knowledge and innovation. For this reason globally open competitions with subsurface data offer an intriguing way of testing the current state of the art in the field of machine learning.

In early 2020 five people from 5 different companies got together to organize the first truly open machine learning competition on well data and seismic under the auspices of FORCE; A cooperating forum of Norwegian Oil&Gas companies and the authorities.

Over 400 truly globally distributed teams and close to 2000 individuals participated in predicting lithology from well logs and identifying faults on seismic.

We will present the results of the competition, compare the machine learning outcomes against expert geological interpretations and disclose the winning teams’ algorithms.

The machine learning competition will well log data attracted most interest with over 300 teams submitting solutions to a tightly contested leaderboard. The top 10 algorithms are very close in terms of their ability to determine lithofacies from well logs alone and the machine predictions are on par or even surpass human interpretations. The fact that none of the algorithms achieved more than 80% correctness is likely a result of the ambiguity in interpretation of closely related lithofacies classes such as shale and silt or chalk and limestone. Still, further improvements to the algorithms, datasets and the way the competition was run are currently being researched by a global community of geodatascientists. The unique provided open dataset will serve as an industry benchmark for the coming years.

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