Data & Analytics
With the latest addition, the Italian major’s computational capacity passes the exaflop threshold, making the firm the world’s leading company by computing power in the new TOP500 global ranking.
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.
This paper presents a novel reservoir engineering/reservoir simulation approach—a data-driven interwell-connectivity model augmented as a digital twin—to predict reservoir dynamics and optimize operations in the Changqing oil field of China.
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This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture. -
The deal follows Blackstone’s recent moves in gas-fired power and a pipeline venture with top US gas producer.
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The collaboration will see TGS’ software platform implemented throughout the carbon value chain at the Northern Lights project.
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By closely monitoring its subsea boosting system, Shell extended maintenance intervals and safely postponed pump replacement at its ultradeepwater Stones field.
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This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.
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This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
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This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.
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With the right infrastructure and interoperability, subsea resident robotics could unlock more frequent, cost-effective inspections—and a new standard for offshore efficiency.
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This work introduces a fast, methodical approach to detect liquid loading using easily available field data while avoiding traditional assumptions and to determine critical gas rates directly from field data.
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This paper describes a tool that complements predictive analytics by evaluating top health, safety, and environment risks and recommends risk-management-based assurance intervention.
