AI/machine learning
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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The authors of this paper apply a deep-learning model for multivariate forecasting of oil production and carbon-dioxide-sequestration efficiency across a range of water-alternating-gas scenarios using field data from six legacy carbon-dioxide enhanced-oil-recovery projects. -
Are we in an AI bubble? The question may seem academic to petroleum engineers who are already capitalizing on the momentum of digitalization across the industry, yet any engineer, regardless of their career stage, could be forgiven for feeling overwhelmed by the sheer scope of specialized skills now demanded in this rapidly evolving digital landscape.
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This paper describes an auto-adaptive workflow that leverages a complex interplay between machine learning, physics of fluid flow, and a gradient-free algorithm to enhance the solution of well-placement problems.
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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.
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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.
