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 new data-management platform is designed to increase access to energy data. -
The US represents the largest regional market, while China is expected to emerge as the fastest-growing market.
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One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This first part of a two-part series presents the history of…
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The carbon footprint of Bitcoin and other cryptocurrencies has been making headlines recently. This article explains what drives their energy consumption and presents alternative approaches.
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The digital project is designed to deliver data management and insight across Aramco’s entire drilling fleet, making it the largest deployment in Baker Hughes’ history.
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Being able to deploy machine-learning applications at the edge is the key to unlocking a multibillion-dollar market. TinyML is the art and science of producing machine-learning models frugal enough to work at the edge, and it's seeing rapid growth.
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Earlier this year, 19 teams competed in a machine-learning contest held by the Data Analytics Study Group of SPE’s Gulf Coast Section. The was the first competition of its kind for SPE. Here, the organizers of the contest present some of the techniques used and lessons learned from the Machine Learning Challenge 2021.
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“What is the drilling state” has become an important question among data scientists and automation experts. The simplest definition of a complicated concept is that it is what the driller is doing at the time.
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Can a camera on the drill floor, or one on a mobile phone, measure what is going on during drilling or evaluate drill-bit wear more consistently than a human?
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The scale of the recent Colonial Pipeline ransomware attack demonstrates why cyber risk should be assessed as a business risk by organizations’ C-suite, going beyond the narrower view of IT/OT network risk.
