Figures Cluster World Oct. 2004 OpendTect article

• For optimal printing quality do not copy Figures 3, 4 and 6a, 6b and 6c from this Powerpoint file. Instead use original tif images!

• The OpendTect logo is delivered in EPS format.

• Contact Paul.deGroot@dgb-Group.com for other formats and/or questions.

Third Party

dGB-Group

Ownership, M&S Responsibility License and M&S fees Waived fees (dGB plugins)

Commercial Users

Academic Users

Commercial Plug-ins

Base

Free Plug-ins

Figure 1. Open Source model.

Figure 2. OpendTect impression.

Figure 3. Geometrically consistent tracking of horizons and faults.

O

O

O

O

O

O

111

1

Energy

Input Attributes: Energy, Frequency, Cube Similarity Continuity, Dip Var., Azimuth Var.,

Absorption, Curvature, ..

Interpreter’s

Knowledge

Meta-Attribute

ANN

Figure 4. “Meta-attribute” concept. Multiple attributes and interpreter’s knowledge are combined by a neural network to give the optimal view of the object of

interest. In this case a salt dome.

Figure 5. Application examples of dip-steering and neural network plugins to OpendTect.

Chimneys

& dGB plugins

Salt

… and turbidites, 4D bodies, ….

Faults

Object

detection

Rock properties

Inversion

Before

After dip-steered median filter

Filtering

Facies / Channels

Patternrecognition

a b c

Figure 6. Generating TheChimneyCube®: a) Picked examples b) neural network performance graphs (RMS error vs. training cycles, percentage mis-classification and input attributes colored to indicate relative importance, with red being

most important) and c) overlay of chimney “probability” on seismic data.