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.

Syllabus

  • 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

Want more information about Paradise Express?



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.