Data & Analytics
Canada’s development of large-scale digital drilling core libraries and open geoscience data platforms follows approaches pioneered in Australia, particularly Western Australia, through decades of government investment in geoscience data collection and preservation.
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.
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Big ideas for digital intelligence in Oil and Gas are being discussed at ATCE. -
Optimization of maintenance costs is among operators’ main concerns in the search for operational efficiency, safety, and asset availability. The ability to predict critical failures emerges as a key factor, especially when reducing logistics costs is mandatory.
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The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting is often related to the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach to resolve this difficulty.
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Managing an unconventional asset requires the proper analysis of large amounts of data. How can unconventional asset operators utilize data processing technologies commonly used in other industries?
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The oil and gas industry is facing an invasion of data analytics startups who saw a wide-open gap in the market a few years ago when talk of big data first began.
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A newcomer in the arena of oilfield market research has set an ambitiously high bar for itself: to speed up the oil and gas industry’s widely acknowledged and painfully slow rate of technology adoption.
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The use of intelligent software is on the rise in the industry and it is changing how engineers approach problems. A series of articles explores the potential benefits and limitations of this emerging area of data science.
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The use of intelligent software is on the rise in the industry and it is changing how engineers approach problems. A series of articles explores the potential benefits and limitations of this emerging area of data science.
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Distributed temperature sensing (DTS) is the most common fiber-optic measurement used for steam-assisted-gravity-drainage reservoir monitoring.
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At the 2016 Gulf of Mexico Deepwater Technical Symposium in New Orleans, a presentation discussed the application of sensors and analytics in pipeline integrity management systems.
