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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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Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Geophysical Insights
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Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of ...
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2D Color Map

First Steps in the Sub-Seismic Resolution of the Eagle Ford, Part I

Geophysical Insights
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Using machine learning to analyze 5 instantaneous attributes helped reveal patterns across 5 instantaneous attributes and unique Eagle Ford facies ...
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Resolution of Faults in the Eagle Ford, Part 3

Resolution of Faults in the Eagle Ford, Part 3

Geophysical Insights
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Applying Principal Component Analysis (PCA) and Self-Organizing Map (SOM) process to show faults on the base amplitude seismic survey and ...
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