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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Norway’s USV joint venture will launch its first unmanned offshore surface vessel in 2025 to support subsea inspection, maintenance, and repairs. -
The industry's increasing dependence on digital systems has escalated the importance of robust cybersecurity strategies, presenting an array of unprecedented challenges.
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SPE and Project Innerspace are organizing the first Geothermal AInnovation Competition. Teams from around the world are invited to participate in this virtual competition aimed at showcasing the potential of AI-assisted work flows in the geothermal life cycle.
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The Brazilian major said it plans to integrate the software throughout its exploration, development, and production operations.
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The US-based drilling contractor is buying the drilling technology company just three weeks after announcing a merger with NexTier Oilfield Solutions.
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Cyber risks facing the oil and gas industry continue to grow. Legal requirements, likewise, are continuing to expand. This article summarizes how these trends may affect oil and gas companies and describes steps companies can take to stay ahead of the curve.
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This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.
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This paper presents an intelligent tube solution that combines data retrieved by the sensors with the actual resistance of each pipe in the well to allow adjustment of production parameters while ensuring installation safety.
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Leading energy technology companies unite to unlock efficiencies and increase reserve recoveries using digital advances.
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The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.
