Geobodies in Paradise: a Machine Learning Application

Geobodies in Paradise: a Machine Learning Application

Geophysical Insights
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Dr. Tom Smith presents "Geobodies in Paradise: a Machine Learning Application" at the 2018 SEG Convention in Anaheim, California. Dr ...
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Making Sense of Machine Learning

Making Sense of Machine Learning

Geophysical Insights
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Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning ...
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Attribute Selection: Machine Learning vs. Interactive Interpretation

Attribute Selection: Machine Learning vs. Interactive Interpretation

Geophysical Insights
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Dr. Kurt Marfurt, Principal Investigator at the AASPI Consortium at the University of Oklahoma, shares insights on "Attribute Selection: Machine ...
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machine learning in seismic interpretation

Comparison of Seismic Amplitude to SOM Classification

Geophysical Insights
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Compare traditional seismic interpretation results with SOM (Self-Organizing Maps) classification achieved with machine learning in Paradise software ...
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Paradise 3.2

Paradise 3.2

Geophysical Insights
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Geophysical Insights announces the launch of Paradise 3.2, which includes the isolation of geobodies through machine learning, and the generation ...
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Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data

Geophysical Insights
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Deborah Sacrey, Owner and Geophysicist of Auburn Energy, provides a review of the various attribute categories and their possible machine ...
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Honeycomb Default

Machine Learning Terms

Geophysical Insights
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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

Thin Beds and Anomaly Resolution in the Niobrara

American Oil and Gas Reporter (AOGR)
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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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Attribute Essentials: Categories of Attributes

Attribute Essentials: Categories of Attributes

Geophysical Insights
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A review of the various attribute categories and their possible application ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning

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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Seismic Interpretation of DHI Characteristics with Machine Learning

Seismic Interpretation of DHI Characteristics with Machine Learning

Geophysical Insights
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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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3-d seismic image

Machine Learning Revolutionizing Seismic Interpretation

American Oil and Gas Reporter (AOGR)
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The science of petroleum geophysics is changing, driven by the nature of the technical and business demands facing geoscientists as ...
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Interpretation of DHI Characteristics with Machine Learning

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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Seismic Interpretation Below Tuning with Multiattribute Analysis

Seismic Interpretation Below Tuning with Multiattribute Analysis

The Leading Edge
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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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