Direct Hydrocarbon Indicators
Future of Seismic Interpretation with Machine Learning and Deep Learning
Geophysical Insights
Dr. Ivan Marroquin discusses the use of deep learning, machine learning, and big data to advance seismic interpretation processes for ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning
SPE Norway
Geophysicists, Rocky Roden & Patricia Santogrossi, discuss machine learning applications enabling refined assessment of thin beds and DHI characteristics ...
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Seismic Interpretation of DHI Characteristics with Machine Learning
Geophysical Insights
The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this ...
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Interpretation of DHI Characteristics with Machine Learning
First Break
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 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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Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators
Geophysical Insights
Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of ...
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Using Self-Organizing Maps to Explore the Yegua in the Texas Gulf Coast
Geophysical Insights
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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Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps
Interpretation Journal
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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Distillation of Seismic Attributes to Geologic Significance
Offshore Technology Conference
Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes ...
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