AI/machine learning
The emerging vision is an industry where engineers focus on judgment, risk assessments, and accountability.
Novel lift systems, AI-driven automation, and real-time engineering models are helping operators extract more value from mature wells.
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
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This case study from Tomax outlines the use of artificial intelligence to analyze high-fidelity data of the full drilling process. -
This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.
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Speakers at ATCE said innovative technologies will play a critical role in balancing energy security, affordability, and sustainability in the years ahead.
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Known as Arena, the collaboration aims to improve decision-making across increasingly complex reservoirs and growing volumes of subsurface data.
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Robotic and autonomous systems are transforming energy operations, but their full value emerges only when paired with data science, engineering analytics, and AI/ML to turn data into decisions. The transition of the SPE Robotic & Autonomous Systems Technical Section into the SPE Data Science & Engineering Analytics Technical Section helps bridge this critical technolo…
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A credible custody-transfer record depends on far more than the accuracy of a single meter. It is the product of an integrated measurement chain. While meter performance supports measurement-system integrity, commercial confidence also depends on traceability, governance, and authorization.
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This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
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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.
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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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After years of market shocks, technological breakthroughs, and rising uncertainty, ATCE 2026 will provide new insights on how industry leaders and technical experts are preparing for the next era of the upstream business.
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