ANALYZE RESERVOIR PROPERTIES USING MACHINE LEARNING

Reveal Facies and Faults Automatically

Detect Thin Beds Below Classic Seismic Tuning

Visualize Direct Hydrocarbon Indicators (DHIs)

Machine Learning Essentials for Seismic Interpretation

Presenter: Dr. Tom Smith

Geophysical Society of Houston (GSH) Webinar

Significant Advancement in Seismic Reservoir Characterization with Machine Learning

Expanded SOM results in seismic interpretation software

Rocky Roden and Patricia Santogrossi

The application of machine to classify seismic attributes at single sample resolution enhancing reservoir characterization.

Compare Seismic Amplitude to SOM Classification

Check out these striking comparisons of SOM classifications in various geologic settings to original amplitude data

What is Machine Learning?

Coined in 1959 by Arthur Samuel, “Machine learning (ML) is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.”

Wikipedia, 2018

Are you new to Paradise?

FEATURED RESOURCES

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

Thin Beds and Anomaly Resolution in the Niobrara

Thin Beds and Anomaly Resolution in the Niobrara

  Download PDF Identifying thin beds using machine learning In March of 2018, a study was conducted of the Niobrara ...
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What is Machine Learning?

What is Machine Learning?

If you’re new to Machine Learning, let’s start at the top. The whole field of artificial intelligence is broken up ...
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Significant Advancements in Seismic Reservoir Characterization with Machine Learning

Significant Advancements in Seismic Reservoir Characterization with Machine Learning

  Download PDF By: Rocky Roden and Patricia Santogrossi Published with permission: The First - SPE Norway Magazine Volume 3 ...
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New Tools for Interpretation

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

PRINCIPAL COMPONENT ANALYSIS (PCA)

Identify attributes that have the greatest contribution to the region according to their relative variance

SELF-ORGANIZING MAP (SOM)

Probe, analyze, and understand classification results in the Universal Viewer to refine an interpretation

INTERACTIVE 2D COLORMAP

Run different SOM configurations to extract greater information from multiple attributes simultaneously

UNIVERSAL VIEWER

Display 2D and 3D views of data while using the 2D Colormap to gain understanding of classification results

GEOBODY ANALYSIS

Reveal the attributes that correspond to a geobody according to their relative contribution through the SOM process

“…machine learning software in Paradise is applied to seismic attributes to find patterns and important geology… [and] Self-Organizing Maps are used to analyze data at single sample resolution.”

— American Oil & Gas Reporter

Guided ThoughtFlowsTM

Paradise enables every interpreter to use powerful machine learning processes through straight forward, left-to-right guided workflows. Learn more about how to set up and generate a PCA chart of attributes and SOM classification results, then use the unique 2D Colormap with the 3D Viewer to interpret geobodies.

“Paradise distills a variety of information from many attributes simultaneously at single sample resolution… This is one of the many differences in the application of machine learning and pattern recognition methods available in Paradise.”

— GEO ExPro

Read case studies on the application of machine learning processes, including Self-Organizing Maps (SOM’s) and Principal Component Analysis (PCA), as applied to seismic attributes in various geologic settings, including onshore – conventional and unconventional – and offshore.

Click here for Case Studies & Technical Papers

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More information about machine learning
More information about Paradise
More information on attributes
Identifying DHIs using SOM
Identifying thin beds / interpreting below tuning
Identifying geobodies using SOM
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