AI/machine learning
The Norwegian major said it is using artificial intelligence for predictive maintenance throughout its facilities and for interpretation of seismic data from the Norwegian continental shelf.
This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
Are we in an AI bubble? The question may seem academic to petroleum engineers who are already capitalizing on the momentum of digitalization across the industry, yet any engineer, regardless of their career stage, could be forgiven for feeling overwhelmed by the sheer scope of specialized skills now demanded in this rapidly evolving digital landscape.
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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. -
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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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.
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This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.
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This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities.
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Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.
