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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Second only to the power and utility sector, the oil and gas industry is experiencing a higher frequency of cyber attacks than any other industry. The vast majority of penetrations are in the information technology (IT) networks that run a company’s daily business. -
More than 45% of energy companies fell victim to at least one cyberattack in 2014, a higher percentage than in any other corporate sector. With constant hacking threats, companies must develop strong cybersecurity strategies.
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Use of unmanned aerial vehicles to monitor pipeline networks for theft and other issues is discussed.
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Since 2007, an operator in Nigeria has registered a significant increase of oil-spill events caused by sabotage and oil-theft activities. The technology presented here allows detecting and locating leaks taking place at a distance from the sensor of up to 35 km.
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For thin-oil-rim reservoirs, well placement, well type, well path, and the completion methods must be evaluated with close integration of key reservoir and production-engineering considerations.
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The big data approach will allow new types of data-driven models to bypass traditional bottlenecks. It is also expected to lead to different views of standard models, thus providing new and valuable insights in the process.
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Many business and digital corporations claim that between 100 billion and 200 billion devices could be connected by 2020.
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A data-driven approach to successfully analyze and evaluate production-fluid impact during facility system divert events is presented. The work flow effectively identifies opportunities for prompt event mitigation and system optimization.
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Failure to prioritize objectives and improper selection of candidate wells can have significant implications for both derived value and potential risk.
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Data mining for production optimization in unconventional reservoirs brings together data from multiple sources with varying levels of aggregation, detail, and quality.
