Data & Analytics
The Norwegian major said it is using artificial intelligence for predictive maintenance throughout its facilities and for interpretation of seismic data from the Norwegian continental shelf.
Experience in subsurface production and lift design is shaping a new generation of geothermal operations built for reliability and scalability.
This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
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This paper describes a decision-support system that integrates field data, system specifications, and simulation tools to quantify system performance, forecast operational challenges, and evaluate the effect of system modifications in water management. -
This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes.
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GeoMap Europe is the latest in a series of interactive global geothermal maps that combine large subsurface and surface data sets to highlight where geothermal resources and development opportunities are strongest for power, heat, cooling, and storage.
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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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Geophysicist Markos Sourial discusses advances in seismic imaging, the challenges of modern data processing, and what they mean for the next wave of subsurface professionals.
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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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SPE and The Open Group have signed a memorandum of understanding to advance collaboration and innovation in the global energy industry.
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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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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 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.
