machine learning
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Increasing accuracy in models is often obtained through the first steps of data transformations. This guide explains the difference between the key feature-scaling methods of standardization and normalization and demonstrates when and how to apply each approach.
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There is often an assumption that big data, together with machine learning, will solve whatever problems asset-heavy industries such as oil and gas face. This is not the case; big data alone isn’t enough. We need something else to solve these problems, and the answer lies in the world of physics.
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In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection.
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This paper discusses a prescriptive analytics framework to optimize completions in the Permian Basin.
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MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.
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The complete paper explains the steps taken to improve surveillance of beam pumps using dynamometer-card data and machine-learning techniques and reviews lessons learned from executing the operator’s first artificial intelligence project.
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Many predictions have been made about what advances are expected in the field of artificial intelligence and machine learning. This column reviews a “data set” based on what researchers were apparently studying at the turn of the decade to take a fresh glimpse into what might come to pass in 2020.
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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.
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This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
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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.