AI/machine learning
As carbon capture scales up worldwide, the real challenge lies deep underground—where smart reservoir management determines whether CO₂ stays put for good.
This article is the third in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Zikri Bayraktar, a senior machine learning engineer with SLB’s Software Technology and Innovation Center, discusses the expanding use of artificial intelligence in the upstream sector.
This article presents a results-driven case study from an ongoing collaboration between a midstream oil and gas company and Neuralix Inc.
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This story reviews well-adopted ideas that have stood the test of time, presenting a small set of techniques that covers a lot of basic knowledge necessary to understand modern deep learning research. -
In industries where data is key to gaining competitive advantage, artificial intelligence and machine learning have become necessities. Tim Custer, senior vice president with Apache, shares how artificial intelligence is affecting the way the energy business operates.
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An intelligent drilling optimization application performs as an adaptive autodriller. In the Marcellus Shale, ROP improved 61% and 39% and drilling performance, measured as hours on bottom, improved 25%.
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The 5-year effort will take advantage of Microsoft technologies in machine learning, augmented reality, user interactions, and the industrial Internet of things to deliver integrated solutions to the upstream industry.
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This policy brief explores the key issues in attempting to improve cybersecurity and safety for artificial intelligence as well as roles for policymakers in helping address these challenges.
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Research shows how to understand the role of individual neurons in a neural network.
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Like biological brains, artificial neural networks may depend on slow-wave sleep for learning.
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With their gee-whiz—albeit artificial—intelligence, robots may be the industry’s answer to jobs deemed dangerous, dirty, distant, or dull.
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Machine learning enables fast, cost-effective, and accurate methane emissions detection in remote areas.
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Artificial intelligence just seems to get smarter and smarter. But some of the improvement comes from tweaks rather than the core innovations their inventors claim—and some of the gains may not exist at all, says Davis Blalock, a computer science graduate student at MIT.
