AI/machine learning
The DOE-backed EGS-Twin project aims to simulate geothermal production systems, helping operators better predict performance and maximize output.
From AI-enabled walking rigs to subsea drones and autonomous inspection robots, robotics is rapidly moving from pilot projects to field deployment. The technology promises not only greater efficiency but also a fundamental shift in how the industry approaches safety and asset management.
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
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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. -
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.
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This paper focuses on developing a model that can be used in an automated, end-to-end flare-smoke detection, alert, and distribution-control solution that leverages existing flare closed-circuit television cameras at manufacturing facilities.
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This paper describes an experimentation trial deploying and operating a computer-vision system on a deepwater rig to measure drilled cuttings in real time using a remotely monitored camera system.
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Real-time wellhead monitoring aims to help Romania meet new EU methane emission regulations.
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The supermajor’s Energy Outlook 2025 suggests geopolitical fragmentation could tilt the balance of the energy trilemma toward energy security and away from sustainability.
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The companies said they plan to start deploying digital twin technologies in Oman this year.
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Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.
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This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.
