Data & Analytics
SLB's and Baker Hughes' partnerships with NVIDIA and Google Cloud, respectively, will develop advanced AI-enabled power optimization and sustainability solutions for the global data center sector.
ExxonMobil's Jason Gahr uses the five stages of grief to explain how the upstream industry should respond to the rise of AI.
This paper introduces an agentic artificial-intelligence framework designed for offshore production surveillance and intervention.
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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. -
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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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.
