Data & Analytics
Canada’s development of large-scale digital drilling core libraries and open geoscience data platforms follows approaches pioneered in Australia, particularly Western Australia, through decades of government investment in geoscience data collection and preservation.
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
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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. -
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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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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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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In this workshop, experts from all disciplines will discuss how science is being applied to optimize the development of unconventional resources—and what challenges remain to be solved.
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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.
