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Net Reservoir Discrimination thumbnail

Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

First Break
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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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Carrie URTeC paper

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

Unconventional Resources Technology Conference
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In a paper presented at URTeC 2019, Geophysical Insights uses Paradise machine learning software to improve resolution the reservoir intervals ...
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60th Annual Exploration Meeting

Applications of Machine Learning for Geoscientists – Permian Basin

Geophysical Insights
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Carrie Laudon, Senior Geoscience Consultant with Geophsyical Insights, explores new Machine Learning applications in E&P for geoscientists in the Permian ...
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Seismic Facies Classification Using Deep Convolutional Neural Networks

Seismic Facies Classification Using Deep Convolutional Neural Networks

SEG Annual Meeting
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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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Image of Seismic amplitude co-rendered Lines A and B

A Fault Detection Workflow Using Deep Learning and Image Processing

SEG Annual Meeting
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Tao Zhao unveils a fault detection workflow using deep learning and image processing technologies at the 2018 Annual SEG Meeting ...
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Solving Exploration Problems with Machine Learning featured-image

Solving Exploration Problems with Machine Learning

First Break
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Geoscientists Deborah Sacrey and Rocky Roden solve exploration problems using Paradise, machine learning software for seismic interpretation in the June ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning

SPE Norway
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The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir ...
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Geobody Interpretation Through Multi-Attribute Surveys, Natural Clusters and Machine Learning

Geobody Interpretation Through Multi-Attribute Surveys, Natural Clusters and Machine Learning

Geophysical Insights
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This paper sets out a unified mathematical framework for the process from seismic samples to geobodies ...
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Image of conventional stacked seismic amplitudes

Interpretation of DHI Characteristics with Machine Learning

First Break
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Applying Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in sub-seismic resolution to reveal facies and shale ...
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