Digital Oil Field
This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.
This paper presents the large-scale deployment and operational integration of an advanced, automated rheometer in the Ghawar gas field, one of the world’s most prolific and technically complex hydrocarbon developments.
The Seismos full-scale research center opens to industry, universities, and national laboratories, advancing acoustic sensing across upstream energy, pipelines, critical infrastructure, and national security.
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This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps. -
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
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This paper presents the deployment of an artificial intelligence-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control with cloud-based analytics to enhance artificial gas lift performance in producer wells.
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The deals, with a potential value exceeding $3.7 billion, are aimed at strengthening Saudi Aramco’s supply chains and expanding its use of industrial artificial intelligence.
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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 competency-centered, data-driven approach implemented to strengthen control-room emergency-response capability through a cloud-hosted, scenario-based virtual plant simulator.
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This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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This paper describes an emergency-response system deployed on artificial islands that manages complex challenges, enhances safety protocols, and improves operational efficiency in high-risk offshore environments.
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This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
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This paper introduces in-pipe inspection technologies enabling high-resolution digital measurements of tubular internal diameter and wall thickness for critical downhole applications.
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