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Visualization of tight carbonate reservoirs

Visualization and Characterization of Paleozoic (Ordovician-Devonian) Tight Carbonate Reservoirs, Oklahoma, Part 2

Geophysical Insights
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Part 2 of a 2-part Paradise Application Brief series applying multiple seismic attributes to enable easy high-grading of leaseholds, asses ...
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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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Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

Detailed Sub-Seismic Resolution in the Eagle Ford Shale and Identification of Under-Explored Geobodies, Part 2

Geophysical Insights
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Results of a Self-Organizing Map (SOM) of many instantaneous attributes to reveal different types of facies and shale that apply ...
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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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Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Stratigraphic and Structural Resolution Using Instantaneous Attributes on Spectral Decomp Sub-Bands, Buda and Austin Chalk Formations, Part 4

Geophysical Insights
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Concurrent analysis of multiple attributes through machine learning to spectral decomposition sub-bands and other geology that apply attributes for stratigraphic ...
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multi attribute analysis for unconventionals

Attribute Analysis in Unconventional Resource Plays Using Unsupervised Neural Networks

Geophysical Insights
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A case study of 10 square mile Eagle Ford Shale Trend utilizing machine learning in Paradise to apply inversion and ...
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Self Organizing Map SOM - Attributes

Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast

Geophysical Insights
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Exploring shallow Yegua formation as an independent method to accurately identify anomalies and exposing direct hydrocarbon indicators using Self-Organizing Map ...
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BEG Line 53 Full Classification - Copy

Self-Organizing Neural Nets for Automatic Anomaly Identification

Geophysical Insights
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Self-organizing maps are a type of unsupervised neural network which fit themselves to the pattern of information in multi-dimensional data ...
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