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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Artificial intelligence tools present many opportunities for the energy industry, and, as technological concepts leave the realm of science fiction, companies have started to grasp what is possible. What roles do culture and ethics play in helping companies understand the digital revolution? -
Recently, AI researchers from Microsoft open-sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine-learning models based on blockchain technologies.
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This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant.
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Baker Hughes is still a GE company, but it has partnered with a second company for artificial intelligence expertise, C3.ai. The deal is expected to speed the integration of AI into oilfield operations by the company which also markets GE’s device analytics platform, Predix.
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Baker Hughes, a GE company, (BHGE) and C3.ai announced a joint venture agreement that brings together BHGE’s fullstream oil and gas expertise with C3.ai’s unique artificial-intelligence software suite to deliver digital transformation technologies and drive productivity for the oil and gas industry.
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Researchers at the University of Massachusetts, Amherst, performed a life-cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 lbm of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car.
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Random Forest and Neural Network are the two widely used machine-learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?
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Malaysia’s Petronas, Shell Malaysia, and Thailand’s PTTEP are now in the midst of full-scale digital adoption. The companies are beginning to see results, but none is counting on a “big bang” in development of the technology soon.
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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.
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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.
