Data & Analytics
The emerging vision is an industry where engineers focus on judgment, risk assessments, and accountability.
This paper presents the large-scale deployment and operational integration of an advanced, automated rheometer in the Ghawar gas field, one of the world’s most prolific and technically complex hydrocarbon developments.
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
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The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields. -
This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.
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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.
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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.
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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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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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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.
