Data & Analytics
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
This case study presents the first field deployment of acoustic fracturing analysis to evaluate perforation efficiency and implement real-time interventions during pressure pumping.
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As managed pressure drilling gains traction, its principles are influencing a wider range of well-construction activities. -
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.
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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 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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The AI revolution isn't just digital. This article examines how geology underpins the convergence of AI, energy, carbon management, and the infrastructure powering frontier LLMs.
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SponsoredEffective data analysis depends on seeing connections that matter. Explore how Spotfire enables a more intuitive approach to understanding petroleum data.
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Schneider Electric says the deal advances its vision of creating intelligent industrial ecosystems that connect physical assets with digital insights across the asset life cycle.
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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.
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This case study describes how edge computing and industrial internet of things platforms were deployed to automate and optimize production operations across four distinct basins.
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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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