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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Leading corporations seem to be failing in their efforts to become data-driven. This is a central and alarming finding of NewVantage Partners’ 2019 Big Data and AI Executive Survey. -
BP Ventures has invested $5 million in Belmont Technology’s Series A financing to further bolster BP’s artificial intelligence and digital capabilities in its upstream business.
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The technology is being proven in millions of phones and homes across the world. Now, a small group of software startups wants to introduce chat bot technology to oil and gas professionals.
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Royal Dutch Shell is heavily investing in research and development of artificial intelligence, which it hopes will provide solutions to some of its most pressing challenges.
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Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry’s 150,000 US workers.
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Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health.
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An increasingly buzzy term tossed around at industry events, “digital twin” is leveraging data analytics, machine learning, and artificial intelligence to improve efficiencies from design to decommissioning.
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As shale plays are becoming economically viable, operators have fast-adopted best practices to optimize drilling and completion processes to drive down the lifting costs. Adoption of data-driven analytics to improve completion design, drive efficiency, and yield economic gains has been less swift.
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Digitalization in the oil and gas industry has been the focus of much discussion, but little has been written on the slow rate of adoption. This paper outlines some of the barriers the industry faces as it assimilates into Industry 4.0—automation and data integration in manufacturing.
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BHGE is developing an analytics and machine-learning approach that offers descriptive and predictive insights on frac hits, with the aim of eventually offering a real-time monitoring capability to be deployed during frac jobs.
