Figures Cluster World Oct OpendTect article

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Presentation transcript:

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.

Figure 1. Open Source model. 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.

Input Attributes: Meta-Attribute ANN O 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.

Object detection Inversion Filtering Pattern recognition Rock Chimneys & dGB plugins Salt … and turbidites, 4D bodies, …. Faults Object detection Rock properties Inversion Before After dip-steered median filter Filtering Facies / Channels Pattern recognition Figure 5. Application examples of dip-steering and neural network plugins to OpendTect.

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.