AI/machine learning
The Energy and AI Observatory aims to use up-to-date information on energy demand from data centers to determine how artificial intelligence is optimizing the energy sector.
As carbon capture scales up worldwide, the real challenge lies deep underground—where smart reservoir management determines whether CO₂ stays put for good.
This article is the third in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Zikri Bayraktar, a senior machine learning engineer with SLB’s Software Technology and Innovation Center, discusses the expanding use of artificial intelligence in the upstream sector.
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Arundo Analytics has built an integrated industrial Internet of things platform that allows data scientists to productize data-science solutions and accelerate feedback/improvement iterations between end-users and data scientists effectively. -
Microsoft announced three new services that aim to simplify the process of machine learning—an interface for a tool that automates the process of creating models; a new no-code visual interface for building, training, and deploying models; and hosted Jupyter-style notebooks for advanced users.
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Analytics, sensors, and robots are changing the way one of the world’s largest oil and gas companies does business. Underpinning all the new technology though is a shift in how BP thinks, and what it means to be a supermajor in the 21st century.
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Maybe we don’t need to look inside the black box after all. Maybe we just need to watch how machines behave, instead.
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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.
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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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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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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.
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New funding for a chatbot technology, or smart assistant, represents the latest development in the Norwegian operator’s drive toward digitalization.
