AI/machine learning
Known as Arena, the collaboration aims to improve decision-making across increasingly complex reservoirs and growing volumes of subsurface data.
Robotic and autonomous systems are transforming energy operations, but their full value emerges only when paired with data science, engineering analytics, and AI/ML to turn data into decisions. The transition of the SPE Robotic & Autonomous Systems Technical Section into the SPE Data Science & Engineering Analytics Technical Section helps bridge this critical technolo…
A credible custody-transfer record depends on far more than the accuracy of a single meter. It is the product of an integrated measurement chain. While meter performance supports measurement-system integrity, commercial confidence also depends on traceability, governance, and authorization.
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"Sooner or later, we will get machines that are at least as intelligent as humans are," says Christof Koch, chief scientist and president of the Allen Institute for Brain Science in Seattle, Washington. -
This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights.
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Increasing accuracy in models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature-scaling methods of standardization and normalization and demonstrates when and how to apply each approach.
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Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input.
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There is often an assumption that big data, together with machine learning, will solve whatever problems asset-heavy industries such as oil and gas face. This is not the case; big data alone isn’t enough. We need something else to solve these problems, and the answer lies in the world of physics.
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The AI journey starts with a single step, but too many companies take the wrong first step.
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AltaML has announced a partnership with engineering and design firm Kleinfelder in which the two companies will pair 3D reality scans of facilities with artificial intelligence to look for potential problems and risks.
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When the field emerged at the end of the 20th century, it was hoped that computers would be able to operate on their own, with human-like abilities—a capability known as generalized AI.
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As deep learning matures and moves from the hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.
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For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge. So are the challenges.
