AI/machine learning
Aurora Innovation and Detmar Logistics have inked a deal for 30 autonomous trucks that will begin hauling sand in the region next year.
Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
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In oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
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The algorithms for running AI applications have been so big that they’ve required powerful machines in the cloud and data centers, making many applications less useful on smartphones and other edge devices. Now, that concern is quickly melting away, thanks to a series of recent breakthroughs. -
This paper describes a path to general artificial intelligence (AI) (i.e., AI that is as smart or smarter than humans) based on the trend in machine learning that hand-designed solutions eventually are replaced by more-effective, learned solutions.
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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.
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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.
