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 freely accessible online platform is the latest in a series of maps designed to reveal the continent’s untapped geothermal potential. -
The companies say they plan to use AI to unlock value from terabytes of data upstream and across FPSO operations.
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This paper proposes how the strengths of cloud computing can become key enablers for oil and gas organizations in helping them enhance their overall security posture and manage risks within operational-technology environments.
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With artificial intelligence and online training both on the rise, it's crucial to ensure workers and companies treat safety as more than a box-checking exercise.
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The technology to desalinate and reuse produced water for cooling AI data centers in the Permian Basin exists, but addressing cost challenges remains critical to widespread adoption.
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In today’s era of asset management, digital twins are changing risk management, optimizing operations, and benefitting the bottom line.
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Digitalization and advanced analytics have enabled drilling automation that is changing the way wells are executed to deliver more production earlier.
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This paper presents a case study highlighting the demonstration, refinement, and implementation of a machine-learning algorithm to optimize multiple electrical-submersible-pump wells in the Permian Basin.
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This paper presents a closed-loop iterative well-by-well gas lift optimization workflow deployed to more than 1,300 operator wells in the Permian Basin.
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This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.
