AI/machine learning
The companies said they plan to start deploying digital twin technologies in Oman this year.
Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.
This paper reviews the motivation and development of response-based forecasting from the perspective of the authors, reviewing examples and processes that have served as validation and led to modeling refinement.
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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. -
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.
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This paper investigates the most important independent variables, including petrophysics and completion parameters, to estimate ultimate recovery with a machine-learning algorithm. A novel machine-learning model based on random forest regression is introduced to predict estimated ultimate recovery.
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What is explainability in artificial intelligence, and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
