AI/machine learning
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
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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The authors present an artificial-intelligence and machine-learning technology to obtain a high-level, comprehensive view of all equipment in a facility to detect and map corrosion.
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This paper provides an alternative solution to identifying, classifying, and vertically distributing fractures and a lateral distribution method for reservoir modeling.
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The paper demonstrates the ability of deep-learning generative models to enable new shale-characterization methods.
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This paper describes a method to determine rig state from camera footage using machine-learning-based vision-analytics approaches.
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This paper describes the current challenges faced by energy companies, the implications of observable industry trends, the characteristics that potential cybersecurity solutions must meet, and how artificial intelligence (AI) and machine learning (ML) can meet these requirements.
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The machine-learning techniques applied in this study aim to deliver a fouling-prediction model based on both simulation and real-time field data.
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The register aims to help the maritime industry embrace technology advances in artificial intelligence.
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The most promising AI approach you’ve never heard of doesn’t need to go big.
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The ethics of artificial intelligence (AI) has become an important topic in the application of AI and machine learning in the past several years. This second part of a two-part series presents the relevance and use of the ethics of AI in engineering applications. Part 1 explains the evolution and importance of AI ethics.
