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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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. -
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.
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The digital twin aims to allow Petrobras to optimize system settings to maximize production, increase recovery, and reduce risk.
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The memorandum of understanding aims to improve digital work flows in the emerging carbon capture and storage industry.
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It is imperative for energy companies to assess potential legal ramifications of integrating artificial intelligence into their operations.
