Data & Analytics
Shell has demonstrated drone-in-a-box operations from its Mars floating production unit, becoming the first operator in the region to secure FAA approval for self-approved offshore beyond-visual-line-of-sight flights. JPT Senior Technology Editor Jennifer Pallanich visited Shell Technology Center Houston to see the system in action.
Panelists at the SPE Subsea Well Intervention Symposium discussed where AI is delivering value today, where risks remain, and how engineers can best determine its usefulness.
As digital technologies become commonplace, industry leaders say core engineering knowledge remains essential for making informed decisions and avoiding costly mistakes.
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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. -
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 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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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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Finding and producing crude and natural gas is far, far removed from the days of acting on a geologist’s hunch or a wildcatter’s gut feeling.
