AI/machine learning
This study demonstrates the transformative potential of physics-informed neural networks as a hybrid framework for multiphase flow measurement.
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
Novel lift systems, AI-driven automation, and real-time engineering models are helping operators extract more value from mature wells.
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Using the supplied data set of cone penetration test results, competing teams had to predict the number of hammer blows required to drive the pile a given unit of depth in the North Sea. -
Artificial intelligence may never match the human brain.
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Artificial intelligence and emerging technologies such as virtual personal assistants and chatbots are rapidly making headway into the workplace. Research and advisory company Gartner predicts that, by 2024, these technologies will replace almost 69% of the manager’s workload.
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Recently, the hype around artificial intelligence and machine learning caused several people to ask me how much of a project is actual machine learning. Based on man-hours spent on the project, I estimate that only about 5% of the effort is spent directly on data-science-related activities.
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Industry 4.0, the latest industrial revolution, has hit the manufacturing sector, building upon the adoption of computers and automation into industrial processes. How well suited is the oil and gas industry to leverage the new autonomous systems that could emerge from this transformation?
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With so many buzzwords surrounding artificial intelligence and machine learning, understanding which can bring business value and which are best left in the laboratory to mature is difficult.
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Despite having some of industry’s most hazardous working environments, a sector that pioneered the adoption of digital technology has been slow to exploit artificial intelligence and machine learning in the area of health and safety.
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This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability.
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Major differences exist between engineering- and nonengineering-related problems. This fact results in major differences between engineering and nonengineering applications of artificial intelligence and machine learning.
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Joelle Pineau, a machine-learning scientist at McGill University, is leading an effort to encourage artificial-intelligence researchers to open up their code.
