AI/machine learning
Major increases in hydrocarbon production require both incremental and revolutionary technologies, industry leaders said during the SPE Hydraulic Fracturing Technology Conference.
This paper presents an automated workflow deployed for scheduling and validating steady-state production-well tests across more than 2,300 wells in the Permian Basin.
This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.
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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 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.
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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.
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Adaptability, collaboration, and digital technologies are all pages in Aramco’s oilfield R&D playbook.
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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.
