Data & Analytics
Schneider Electric says the deal advances its vision of creating intelligent industrial ecosystems that connect physical assets with digital insights across the asset life cycle.
With the latest addition, the Italian major’s computational capacity passes the exaflop threshold, making the firm the world’s leading company by computing power in the new TOP500 global ranking.
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
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The technologies born out of innovative ideas have been critical for advancing deepwater assets in the past, and venture-capital investment helps incubate risk-taking companies developing those technologies. With digitization becoming a greater focus in industry, what role will venture capital play? -
Maybe we don’t need to look inside the black box after all. Maybe we just need to watch how machines behave, instead.
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Robots may not be ready to take over the world just yet, but they are making great strides in the offshore industry. A technical session at this year’s Offshore Technology Conference presented some of the advances, including untethered ROVs and subsea broadband communications.
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Adoption of digital technologies will continue to improve the offshore sector, including improved well efficiency, real-time directional drilling, lower maintenance costs, and safer operations.
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As the oil and gas industry moves more into the machine learning space, Python-conversant petroleum domain specialists will prove to be increasingly valuable to organizations.
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A growing number of oil industry leaders are saying that data sharing across the industry is needed, but change is coming slowly.
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This paper explains how an ultradeepwater drilling contractor is applying real-time analytics and machine learning to leverage its real-time operations center to improve process safety and performance.
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The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.
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Machine learning (ML) finds patterns in data. "AI bias" means that it might find the wrong patterns. Meanwhile, the mechanics of ML might make this hard to spot.
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This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.
