Data & Analytics
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
This paper presents the deployment of an artificial intelligence-enabled autonomous gas lift optimization system, integrating real-time centralized advanced process control with cloud-based analytics to enhance artificial gas lift performance in producer wells.
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This paper presents an approach to management and interpretation of pipeline-integrity data, ensuring integrity, safety, and reliability of the operator’s critical pipelines. -
This paper describes the development of a system for comprehensive mapping and asset registration using a digital-twin approach.
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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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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.
