Data & Analytics
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With operators under pressure to deliver more energy with fewer resources, predictable drilling performance is more important than ever. Discover how a unified digital workflow can turn drilling data into better decisions and consistent execution—providing the foundation for autonomous drilling and more predictable, profitable, and productive wells at scale.
Shell has demonstrated drone-in-a-box operations from its Mars floating production unit, becoming the first operator in the region to secure FAA approval for self-approved offshore beyond-visual-line-of-sight flights. JPT Senior Technology Editor Jennifer Pallanich visited Shell Technology Center Houston to see the system in action.
Panelists at the SPE Subsea Well Intervention Symposium discussed where AI is delivering value today, where risks remain, and how engineers can best determine its usefulness.
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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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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 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 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 work uses a novel pseudosteady-state-based simulation to reduce training-data-generation cost while maintaining high-performance predictions of data-driven proxy models for carbon-sequestration projects.
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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.
