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.
This paper describes a data-driven well-management strategy that optimizes condensate recovery while preserving well productivity.
This paper explores the evolving role of the digital petroleum engineer, examines the core technologies they use, assesses the challenges they face, and projects future industry trends.
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This paper describes a machine-learning approach to accurately flag abnormal pressure losses and identify their root causes. -
Data and impartial viewpoints can help de-risk exploration portfolios and keep resource estimates in check.
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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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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 explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.
