AI/machine learning
The USGS has said up to 19 million tons of lithium reserves are contained in the briny waters of the Smackover formation in Arkansas.
Subject-matter experts from industry and academia advanced distributed fiber-optic sensing technologies and their implementation in flow measurement during a special session.
This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
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The authors present a review of the capabilities of fog computing and its potential in the petroleum industry. -
The machine-learning techniques applied aim to deliver a prediction model based on both simulation and real-time field data. The model tracks and monitors system key performance indicators.
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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.
