Data & Analytics
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.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
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The Unmanned Technical Section has updated its name to the Robotics and Autonomous Systems Technical Section. -
Oil and gas is one of the most hazardous industries to work in. It is also an industry undergoing a revolutionary digital transformation. With changes come challenges and new opportunities. This paper looks at the top digital safety trends that are taking place within the industry.
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By incorporating AI-powered solutions, companies can tailor wellness plans to cater to the diverse needs of their workforce, fostering a more inclusive and supportive environment.
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Investigation into cybermonitoring of industrial control and operating systems used to detect cyberattacks and discern different types of attacks, with the intent to develop risk-based cybersecurity solutions.
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US federal agencies should develop new crosscutting programs to advance the mathematical, statistical, and computational foundations underlying digital twin technologies, says a new report from the National Academies of Sciences, Engineering, and Medicine.
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The two companies have agreed to consider working together on digital assets and semiconductors.
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The authors of this paper describe a suite of technologies that enables enhanced well robustness and performance modeling and monitoring of carbon storage facilities.
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The authors of this paper describe an approach in which all available technologies are combined to improve understanding of reservoir depositional environments.
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This paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.
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The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
