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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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Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

GeoExpro
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Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with ...
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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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Seismic Interpretation Below Tuning with Multi-Attribute Analysis

Geophysical Insights
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This international webinar describes how multi-attribute seismic analysis is applied using the Paradise software to visualize thin beds and facies ...
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Water Oil Contact - Conventional Multi-Attribute Analysis

Approach Aids Multiattribute Analysis

American Oil and Gas Reporter (AOGR)
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How Self-Orgazining Maps (SOM) and Principal Componenrt Analysis (PCA) greatly enhances the interpretation process to identify geology in diffferent settings ...
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seismic interpretation software - SOM

Seismic Pattern Recognition in Shale Resource Plays

E&P Magazine
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Utilizing machine learning via Self-Organizing Maps (SOM) and Principal Component Analysis (PCA) interpretation techniques to help identify sweet spots ...
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Well locations for the DHI Consortium

Relating Seismic Interpretation to Reserve / Resource Calculations

The Leading Edge
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Top 5 class 3 direct hydrocarbon indicator characteristics, top five class 2 DHI characteristics, reasons for failure, implications for resource ...
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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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Image of conventional stacked seismic amplitudes

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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