Data & Analytics
With the growing reliability of uncrewed aircraft, oil companies are now eager to have offshore rigs supplied in a more systematic and autonomous way. So, why haven’t we heard more about using uncrewed craft?
The Saudi Arabian major is finding new ways of using drones to help improve the safety, efficiency, and environmental performance of its operations.
A report from GlobalData shows how digital twins—digital representations of physical assets, systems, people, or processes—are increasingly helping oil and gas companies throughout the life cycle of their operations.
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By incorporating AI-powered solutions, companies can tailor wellness plans to cater to the diverse needs of their workforce, fostering a more inclusive and supportive environment. -
Investigation into cybermonitoring of industrial control and operating systems used to detect cyberattacks and discern different types of attacks, with the intent to develop risk-based cybersecurity solutions.
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US federal agencies should develop new crosscutting programs to advance the mathematical, statistical, and computational foundations underlying digital twin technologies, says a new report from the National Academies of Sciences, Engineering, and Medicine.
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The two companies have agreed to consider working together on digital assets and semiconductors.
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Digital data acquisition has revolutionized the oil and gas industry. Recent trends have seen a significant shift toward the use of legacy data, the integration of various sources of data, and the application of machine-learning techniques, creating a more dynamic and data-driven landscape.
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The authors of this paper describe a suite of technologies that enables enhanced well robustness and performance modeling and monitoring of carbon storage facilities.
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The authors of this paper describe an approach in which all available technologies are combined to improve understanding of reservoir depositional environments.
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This paper presents the proof of concept of artificial-intelligence-based well-integrity monitoring for gas lift, natural flow, and water-injector wells.
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The authors of this paper describe a project aimed at automating the task of cuttings descriptions with machine-learning and artificial-intelligence techniques, in terms of both lithology identification and quantitative estimation of lithology abundances.
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The authors of this paper present a machine-learning-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties.
