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Machine learning for detailed reservoir — Wisting case study Featured

A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description – Wisting case study

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Sharareh Manouchehri, Nam Pham, Terje A. Hellem and Rocky Roden predict lithofacies and reservoir properties using multi-attribute seismic analysis based ...
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Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification

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A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal ...
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Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

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Published in the special Machine Learning edition of First Break, this paper lays out results from multi-attribute analysis using Paradise, ...
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Seismic Facies Classification Using Deep Convolutional Neural Networks

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Using a new supervised learning technique, convolutional neural networks (CNN), interpreters are approaching seismic facies classification in a revolutionary way ...
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Machine Learning Terms

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A glossary defining essential machine learning terms within the seismic interpretation and geoscience community from Principal Component Analysis (PCA) to ...
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Thin Beds and Anomaly Resolution in the Niobrara

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Using machine learning to classify a 100-square-mile seismic volume in the Niobrara, geoscientists were able to interpret thin beds below ...
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What is Machine Learning?

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Machine Learning is a subset of Narrow AI that does pattern classification. It’s an engine – an algorithm that learns ...
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What is Big Data?

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Dr. Tom Smith explains "What is Big Data" and it's impact on the oil and gas industry in this short ...
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Seismic Interpretation of DHI Characteristics with Machine Learning

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The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this ...
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