Data & Analytics
Switching from continuous circulation to cyclic huff-‘n’-puff operation in enhanced geothermal systems can significantly delay thermal breakthrough, sustain higher production temperatures, and improve long-term economic performance.
The two companies say they plan to work together to use agentic AI to increase the capabilities of technical experts.
This article is the first in a Q&A series from the SPE Methane Emissions Management Technical Section (MEMTS) on methane intelligence and how oil and gas teams translate emissions data into credible decisions and measurable reductions.
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SponsoredSince 1997, intelligent completions have transformed reservoir management, but adoption remains limited due to perceived complexity. The industry reached a pivotal moment with the development of next-generation technologies that address longstanding challenges and offer simpler operation solutions. -
The Regulators’ Pioneer Fund has provided the North Sea Transition Authority with funding for two data work streams—a data viewer and an AI chatbot—to facilitate access to the vast amounts of North Sea data.
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Industry experts dissected the challenges in deploying artificial intelligence across the energy sector during a special session at SPE’s Annual Technical Conference and Exhibition.
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AI is transforming the field of cybersecurity, offering new possibilities and challenges for both defenders and attackers, but AI also can introduce new vulnerabilities and risks and raise new ethical, legal, and social issues for cybersecurity.
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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.
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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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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.
