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 challenges facing the oil and gas industry require crossover technologies from other industries such as aerospace, automotive, and medicine to help drive efficiency, boost productivity, and optimize performance. -
Permanent downhole gauges (PDGs) provide vast amounts of pressure-transient and rate data which may be interpreted with improved pressure-transient-analysis (PTA) approaches to gain more knowledge about reservoir dynamics.
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This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations.
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This paper describes the development of “digital-rocks” technology, in which high-resolution 3D image data are used in conjunction with advanced modeling and simulation methods to measure petrophysical rock properties.
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Imagine a machine that could make an automaker competitive by speeding product development, help a jet engine maker create unique parts for more efficient turbines, and allow a baker to quickly create a picture-perfect 3D replica of a flower made of sugar.
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There is a lot of information buried in drilling reports written every day, but little of it appears in computer databases.
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There is talk about digital oil fields and big data and some striking examples of their power. But in real oil fields, a lot of operators are still running fields with systems relying on big paper.
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For thin-oil-rim reservoirs, well placement, type and path, and well-completion methods, should be evaluated with close integration of key reservoir- and production-engineering considerations.
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Artificial-intelligence (AI) -based methods have become mainstream engineering, and we as practitioners need to have a firm understanding of the principles and be ready to apply them when the opportunities arise.
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A new intelligent model that successfully learns from high-dimensional data and effectively identifies high-production areas and optimum lateral-re-entry candidates is presented.
