Data & Analytics
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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Cataloging data can be a minefield, and not knowing exactly what you can do with the data you have could lead to disaster. What steps should companies take to handle legacy data, and what importance does the tracking of entitlements play in data management systems? -
A survey of oil and gas decision-makers by Ernst & Young sheds light on how companies are formulating their digital strategies.
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Royal Dutch Shell is heavily investing in research and development of artificial intelligence, which it hopes will provide solutions to some of its most pressing challenges.
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Blockchain technology may have gotten its start by keeping cryptocurrency traders honest, but its usefulness is expanding. And the oil and gas industry is taking advantage.
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To test your knowledge, answer these 10 questions from Jim Crompton, who teaches petroleum data analytics at the Colorado School of Mines.
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Digital solutions that have made their mark in other industries may foster stronger collaborative environments in various sectors within energy, including equipment maintenance and data management.
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Oil companies are talking big about using data and analytics, but the experts in the field are not sure what their role will be. Birol Dindoruk, SPE's technical director for management and information, talks about ensuring that they have a say.
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A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.
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Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry’s 150,000 US workers.
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Expectations from data analytics in the upstream sector continue to evolve. Although the number and diversity of applications continue to increase, the adoption at the assetwide level faces well-known barriers and challenges.
