Instructor(s): Sarah Stanley, Sr. Geophysicist
Cost: $750

This is a specialized course (“Workshop”) exclusively for geoscientists, who have taken or used the Paradise software and would like an introduction to the new capabilities in version 3.2, as well as a refresher in best practices in the application of the software. In the Paradise Express workshop geoscientists will cover the basics of multi-attribute seismic analysis using the Paradise Suite of Software.


  • How to navigate within Paradise Source and calculation of basic seismic attributes
  • Workflow for Seismic Multi-Attribute analysis
  • How to analyze Principal ComponentsHow to create Self Organizing Maps (SOMs)
  • How to view and interact with SOMs
  • How to detect and edit Geobodies
  • Calibrate Paradise result sets to well data

Topics Covered

  • Introduction of course objectives and expectations
  • Sooner Field Geology basic overview including field parameters spreadsheet
  • Instantaneous seismic attributes generation and use overview
  • Generate all instantaneous attributes on full volume 0-2 seconds
  • Run pca on instantaneous attributes from 0 to 2 seconds
  • Analyze Instantaneous PCA including percent total vs. percent max
  • Run PCA on Inst Attributes Defined by Horizons
  • Define attributes for SOMs using PCA results
  • Analyze two SOMs using 3d viewer with 2d colormap
  • View and interpret SOM results
  • Review SOM Display with another SOM
  • Introduce 2D interactive colormap and 1D colorbars
  • Universal Viewer
  • Geobody detection and viewing
  • Geobody Editor
  • Geobody Volumetrics
  • Well Calibration workflow

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Paradise Essentials

This introductory two day course to Paradise enables geoscientists and engineers to use the major workflows and become acquainted with the basic capabilities of the multi-attribute analysis platform. Students will be trained in applying Principal Component Analysis (PCA) to a group of attributes to identify the most significant attributes in the set, then running, viewing and analyzing Self-Organizing Maps (SOMs) on selected attributes using different neural network configurations.



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