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Oil Reservoir Porosity Prediction Using a Neural Network Ensemble Approach

  • Curtis A. Link
  • Phillip A. Himmer
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

The problem of parameter prediction in a hydrocarbon reservoir is typically accomplished by an interpreter using sparse well information and seismic data. The resulting maps may contain varying levels of uncertainty depending on the experience of the interpreter and the availability and quality of seismic and well data.

This chapter describes a neural network ensemble approach to the interpolation problem. An interval of seismic data representing the zone of interest is extracted from a three-dimensional (3-D) data volume. Combinations of seismic data, complex trace attributes, and geometric data are used as inputs to a multilayer perceptron (MLP) neural network. A series of networks is trained, and results are used to produce final ensembles.

Our results show that realistic prediction maps can be generated using a neural network ensemble approach with input data consisting of raw seismic amplitudes, instantaneous amplitude, and coordinate geometry. Ensemble maps created for subintervals in the zone of interest show consistent features. Also, network testing errors at finer levels of resolution are not unusually large compared to errors for a single layer prediction.

Keywords

Seismic Data Testing Error Seismic Attribute Instantaneous Amplitude Bioclastic Limestone 
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

  1. Link, C. A., 1995, Artificial neural networks for lithology prediction and reservoir characterization: Proceedings of the SPIE: Mathematical Methods in Geophysical Imaging III, 2571, 163–174.Google Scholar
  2. Porter, K. W., 1996, A middle Jurassic Piper Formation measured section, Suction Creek, Bear Paws Mountains, Montana: Montana Bureau of Mines and Geology Report RI 2.Google Scholar
  3. Rogers, S. J., Chen, H. C., Kopaska-Merkel, D. C., and Fang, J. H., 1995, Predicting permeability from porosity using artificial neural networks: AAPG Bulletin, 79, 1786–1797.Google Scholar
  4. Taner, M. T., Koehler, F. and Sheriff, R. E., 1979, Complex seismic trace analysis: Geophysics, 44, 1041–1063.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Curtis A. Link
    • 1
  • Phillip A. Himmer
    • 2
  1. 1.Department of Geophysical EngineeringMontana Tech of the University of MontanaButteUSA
  2. 2.Department of Electrical EngineeringMontana State UniversityBozemanUSA

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