Direct Hydrocarbon Indicators

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Future of Seismic Interpretation with Machine Learning and Deep Learning

Geophysical Insights
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Dr. Ivan Marroquin discusses the use of deep learning, machine learning, and big data to advance seismic interpretation processes for ...
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wiggle trace seismic data

Significant Advancements in Seismic Reservoir Characterization with Machine Learning

SPE Norway
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Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics ...
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Screenshot of interpretation video beginning

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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Conventional Stacked Seismic Amplitude Display

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 figure 10

Seismic Interpretation Below Tuning with Multi-attribute Analysis

The Leading Edge
Seismic interpretation of thin beds below tuning has always been a challenge in the oil and gas industry. A multi-attribute ...
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Seismic Attributes for Attenuation

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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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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Som Results B.2

Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Interpretation Journal
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Analyzing seismic data through geologic pattern recognition methods like Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) in Paradise machine ...
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2D Colormaps

Distillation of Seismic Attributes to Geologic Significance

Offshore Technology Conference
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Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes ...
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