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Machine Learning is changing the way interpretation is done. Find out how geoscientists are using machine learning to reveal unprecedented levels of detail in seismic data.


Select an icon below to learn more about powerful, straightforward ThoughtFlows® in Paradise

ATTRIBUTE SELECTION

Identify attributes having the highest variance and contribution among a set of attributes in a geologic setting

MULTI-ATTRIBUTE CLASSIFICATION

Classify multiple attribute volumes simultaneously utilizing Self-Organizing Maps (SOM), an unsupervised machine learning process

Paradise AI ATTRIBUTE GENERATION

ATTRIBUTE GENERATION

Generate attributes to extract meaningful geological information and as input into machine learning analysis for advanced interpretation

Paradise AI MACHINE LEARNING GEOBODIES

MACHINE LEARNING (ML) GEOBODIES

Estimate the volume of reserves/resources and geologic features

Paradise AI DEEP LEARNING (DL) SEISMIC FACIES CLASSIFICATION

DEEP LEARNING (DL) SEISMIC FACIES CLASSIFICATION

Capture seismic facies based on distinctive seismic amplitude patterns using deep learning technology

Paradise AI DEEP LEARNING (DL) FAULT DETECTION

DEEP LEARNING (DL) FAULT DETECTION

Produce fault probability volumes based on already generated fault engines (models) or from interpreter guided trained engines

“We continue to find new insights in the Utica play using the Paradise AI workbench. The results corroborate nicely with ground truth. Using machine learning is now a key part of our interpretation workflow.”

Randall Hunt
Staff Geophysicist, Range Resources

“Anyone who is involved in prospect evaluation and reservoir characterization should have no problem seeing evidence of the stratigraphic and reservoir facies details that a properly constructed multi-attribute classification can provide.”

Dr. Bob Hardage
Former SEG President

See Paradise in Action at Upcoming Events

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Read case studies on the application of machine learning  and deep learning to seismic interpretation.

Click here for Case Studies & Technical Papers

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