AI/machine learning
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.
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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Aurora Innovation and Detmar Logistics have inked a deal for 30 autonomous trucks that will begin hauling sand in the region next year. -
Sustainability in reservoir management emerges not from standalone initiatives but from integrated, data-driven workflows, where shared models, closed-loop processes, and AI-enabled insights reduce fragmentation and make sustainable performance a natural outcome.
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SponsoredIn oil and gas operations, every decision counts. For more than 2 decades, SiteCom has been the trusted digital backbone for well operations worldwide, driving insight, collaboration, and efficiency.
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This study presents a novel hybrid approach to enhance fraud detection in scanned financial documents.
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This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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Even as industry faces policy and tariff uncertainty, companies view spending on digital transformation as a driver of efficiency.
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The Tela artificial intelligence assistant is designed to analyze data and adapt upstream workflows in real time.
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In this third work in a series, the authors conduct transfer-learning validation with a robust real-field data set for hydraulic fracturing design.
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This research aims to develop a fluid-advisory system that provides recommendations for optimal amounts of chemical additives needed to maintain desired fluid properties in various drilling-fluid systems.
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This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.
