OpendTect dGB Plugins User Documentation version 3.2

dGB Earth Sciences

Copyright © 2002-2008 by dGB Beheer B.V.

All rights reserved. No part of this publication may be reproduced and/or published by print, photo print, microfilm or any other means without the written consent of dGB Beheer B.V. Under the terms and conditions of the OpendTect license agreement license holders are permitted to make hardcopies for internal use.
Table of Contents
1. Introduction
2. Steering
2.1. Background
2.2. Create Steering Data
2.2.1. Import Steering Data
2.2.2. Calculate Steering Data
2.2.2.1. Description
2.2.2.2. Create Steering Cube window
2.2.3. Filter
2.2.3.1. Description
2.2.3.2. Filter steering cube window
2.3. Attributes with steering
2.3.1. Curvature
2.3.1.1. Mean curvature
2.3.1.2. Gaussian curvature
2.3.1.3. Maximum curvature
2.3.1.4. Minimum curvature
2.3.1.5. Most positive curvature
2.3.1.6. Most negative curvature
2.3.1.7. Shape index
2.3.1.8. Dip curvature
2.3.1.9. Strike curvature
2.3.1.10. Contour curvature
2.3.1.11. Curvedness
2.3.1.12. General Remark
2.3.2. Dip
2.3.2.1. Polar dip
2.3.2.2. Azimuth
2.3.2.3. Inline dip
2.3.2.4. Crossline dip
2.3.3. Dip angle
2.3.4. Position
2.3.5. Reference shift
2.3.6. Similarity
2.3.7. Volume statistics
2.4. Benchmark steering cube creation
2.4.1. Speed vs. algorithm and calculation cube size
2.4.2. Visual quality check
2.4.3. Crossline dip attribute
2.4.4. Filtering of the steering cubes
2.4.5. Steered similarity attribute
2.4.6. Choosing a steering algorithm
3. Sequence Stratigraphic Interpretation System
3.1. Introduction
3.1.1. SSIS Toolbar
3.2. Data Preparation
3.2.1. Horizon preparation
3.2.2. Filtering the steering cube
3.3. Chrono Stratigraphy
3.3.1. Create Chrono Stratigraphy
3.3.2. Display Chrono Stratigraphy
3.4. Wheeler Transform / Wheeler Scene
3.4.1. Wheeler Scene
3.4.2. Create Wheeler Cube
3.5. Interpretations
4. Neural networks
4.1. Introduction
4.1.1. Supervised neural networks
4.1.2. Unsupervised neural networks
4.2. Neural network management window
4.3. Neural network information
4.3.1. Supervised neural network information
4.3.2. Unsupervised neural network information
4.4. Import GDI networks window
4.5. New from PickSets
4.6. New from Well Data
4.6.1. Balance Data
4.6.2. NN Lithology codes
4.7. NN training window
4.7.1. Unsupervised training
4.7.1.1. Quick UVQ
4.7.2. Supervised training from pickset
4.7.3. Supervised training from well data
5. Pre-Stack Depth Migration - Velocity Model Building
5.1. Introduction
6. Common Contour Binning (*)
6.1. Introduction
6.2. CCB Main window
6.3. CCB Analysis
7. Applications
7.1. How to Make TheChimneyCube®
7.1.1. Workflow
7.1.2. Picking example locations
7.1.3. Neural network training
7.1.4. Evaluation and application of the trained neural network
7.2. The Dip-Steered Median Filter
7.2.1. Example results
7.2.2. Create the Median Dip Filter yourself
7.2.3. Note
8. Default attribute sets
8.1. Evaluate Attributes
8.2. dGB Evaluate Attributes
8.3. NN Chimney Cube
8.4. NN Fault Cube
8.5. NN FaultCube Advanced
8.6. NN Salt Cube
8.7. NN Slump Cube
8.8. Unsupervised Waveform Segmentation
8.9. Ridge-Enhancement Filter
8.10. Dip-Steered Median Filter
8.11. Dip-Steered Diffusion Filter
8.12. Fault Enhancement Filter
8.13. Fault Enhancement Attributes
8.14. Seismic Filters Median-Diffusion-Fault-Enhancement
9. References