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Mapping the Gas Column in an Aquifer Gas Storage with Neural Network Techniques

  • H. Trappe
  • C. Hellmich
  • J. Knudsen
  • H. Baartman
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 80)

Abstract

An approach using seismic attributes and neural networks to map the gas extent was tested. The study was part of a reservoir project characterising a gas storage. AVO modelling and processing was done beforehand to define the extent of the gas distribution but lead to no clear conclusion. Seismic attributes showed indications of the gas extent but also were not conclusive. The neural network classification integrated three seismic attributes leading to a clearer delineation of the gas extent compared to the AVO results. The case study showed a successful application of neural networks which was used to solve a highly ambiguous problem.

Keywords

Seismic Line Seismic Modelling Pore Fill Seismic Attribute Amplitude Envelope 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Haghon, S., 1994. Neural Networks, A comprehensive Foundation, MacMillan College Publishing Co., NY.Google Scholar
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    Heggland, R., Meldahl, P., de Groot, P. and Aminzadeh, F., 2000. Seismic chimney interpretation examples from the North Sea and the Gulf of Mexico American Oil and Gas Reporter.Google Scholar
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    Trappe, H., Hellmich, C., 2000. Using Neural Networks to Predict Porosity Thickness from 3D Seismic, First Break 17, 377–384.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • H. Trappe
    • 1
  • C. Hellmich
    • 1
  • J. Knudsen
    • 2
  • H. Baartman
    • 2
  1. 1.Trappe Erdoel Erdgas ConsultantIsernhagenGermany
  2. 2.DONG Naturgas A/SDenmark

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