AI/machine learning
The firm’s latest report, Leading a Data-Driven Transition, presents the results of its annual survey of nearly 1,300 senior professionals and divides the respondents into two groups, which it calls “digital leaders” and “digital laggards.”
The industry is balancing brains and bots as it squeezes out barrels of oil production.
Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…
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Both new and old vessels are benefiting from automation processes that can improve operational efficiency, predict downtime, and debottleneck workflows using a flurry of crucial data points. -
Operators tell an audience at the Unconventional Resources Technology Conference how a hybrid expandable liner system and machine-learning-based analysis improve the bottom line.
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Machine learning is refining gas lift production optimization with scalable automated workflow.
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The Permian’s produced-water challenge presents an opportunity for innovation to pave the way toward a more sustainable future for the industry.
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The chief operating officer of Chesapeake Energy tells the Unconventional Resources Technology Conference that small wins can pave the path to big achievements.
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The Norwegian major agrees to use Seeq’s software in an effort to maximize production and enhance efficiency across its assets.
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This article explores the implementation of artificial intelligence vision for leak monitoring automation in the oil and gas industry and its role in improving safety standards, operational efficiency, and environmental performance.
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This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model.
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This study proposes a hybrid model that combines the capacitance/resistance model, a machine-learning model, and an oil model to assess and optimize water-alternating-gas (WAG) injectors in a carbonate field.
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The partnership aims to use artificial intelligence and advanced robotics to accelerate the adoption of technologies for predictive maintenance.
