Data & Analytics
Working with Dell Technologies and NVIDIA, the French supermajor is targeting improved seismic processing and artificial intelligence applications.
A discussion at the inaugural executive breakfast convened by the SPE Data Science and Engineering Analytics Technical Section, held alongside CERAWeek by S&P Global and powered by Black & Veatch, tackled the challenge of value creation from artificial intelligence in the energy industry.
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
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This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models. -
As you read the examples in this section, you will see that a change is already under way in that the methods that are being used are increasingly not oil-and-gas-specific but instead follow patterns that are being used in other industries.
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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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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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The first set of notations of its kind helps owners and operators qualify and use smart functions to manage asset health and performance.
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Representatives from ConocoPhillips, Shell, Chevron, and BP came together onstage at the 2019 Professional Petroleum Data Expo in Houston to present the Open Subsurface Data Universe, “an open-source, data-driven, reference architecture for subsurface and well data in a cloud solution.”
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Demand is growing at the University of Houston and others from students who want to study data science; from researchers who produce, interpret, or otherwise work with reams of data; and from industry, which needs a data science-savvy workforce.
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Rapid advances in deep learning continue to demonstrate the significance of end-to-end training with no a priori knowledge. However, when models need to do forward prediction, most AI researchers agree that incorporating prior knowledge with end-to-end training can introduce better inductive bias.
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Total plans to start a digital factory to tap artificial intelligence in a bid to save hundreds of millions of dollars on exploration and production projects, according to an executive.
