Data & Analytics
The companies said they plan to start deploying digital twin technologies in Oman this year.
This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.
This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities.
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Oil and gas is one of the most hazardous industries to work in. It is also an industry undergoing a revolutionary digital transformation. With changes come challenges and new opportunities. This paper looks at the top digital safety trends that are taking place within the industry. -
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.
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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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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 present a machine-learning-based solution that predicts pertinent gas-injection studies from known fluid properties such as fluid composition and black-oil properties.
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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.
