Data & Analytics
Working with Dell Technologies and NVIDIA, the French supermajor is targeting improved seismic processing and artificial intelligence applications.
A discussion at the inaugural executive breakfast convened by the SPE Data Science and Engineering Analytics Technical Section, held alongside CERAWeek by S&P Global and powered by Black & Veatch, tackled the challenge of value creation from artificial intelligence in the energy industry.
AI‑driven data center growth is straining US power grids and accelerating interest in enhanced geothermal systems as a scalable, low‑carbon solution.
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Declining costs to launch monitoring satellites, as well as artificial intelligence, which makes parsing terabytes of emissions data feasible, have given the oil and gas industry an emerging tool for environmental stewardship. -
This paper focuses on the application of blockchain technology and the use of distributed ledgers on process safety and integrity management.
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The software that the duo is working on aims to optimize and automate the moving of drilling rigs.
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Sustainability data management is a relatively new discipline that requires a unique set of tools, processes, and procedures to meet corporate demands.
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Initial forays into using digital twins across its major fields has inspired the multinational hydrocarbon exploration and production company to further adopt the technology across its entire portfolio.
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The USV will be equipped with a remotely operated vehicle that is capable of operating in water up to 1500 m deep and tools to perform subsea operations.
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The Abu Dhabi National Oil Company said it plans to deploy uncrewed aerial vehicles equipped with the latest imaging technology for detailed inspections of site assets and infrastructure.
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The robot company said it plans to integrate 3D at Depth’s LiDAR inspection technology after the all-stock acquisition.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.
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This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
