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Proceedings Paper

Artificial neural networks for lithology prediction and reservoir characterization
Author(s): Curtis A. Link
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Paper Abstract

Artificial neural networks are becoming increasingly popular as a method for parameter classification and as a tool for recognizing complex relationships in a variety of data types. The power of neural networks comes from their ability to `learn' from a set of training data and then to generalized to new data sets. In addition, neural networks have the potential to assimilate data over a wide range of scales and are robust in the presence of noise. A back propagation neural network has been successfully applied in predicting paleosol sequences using well log suites from two wells in a Cenozoic basin in southwestern Montana. The training set consists of neutron porosity, bulk density, and resistivity logs and the interpreted paleosol section. Training is accomplished using well log values over a range of depths rather than discrete depths. The trained network is used to predict paleosol occurrences in a neighboring well. Network prediction results show good agreement with paleosol interpretations. Neural networks are also being applied as part of a reservoir characterization project for a north central Montana oil field. A back propagation network shows limited success in predicting sonic and porosity well logs from resistivity, gamma, and density logs. Preliminary attempts to predict spatial distribution of porosity from 3D seismic data show some promise. The use of high-pass filtered seismic data and seismic trace attributes improves prediction error over the unfiltered seismic data alone.

Paper Details

Date Published: 1 September 1995
PDF: 12 pages
Proc. SPIE 2571, Mathematical Methods in Geophysical Imaging III, (1 September 1995); doi: 10.1117/12.218494
Show Author Affiliations
Curtis A. Link, Montana Tech (United States)

Published in SPIE Proceedings Vol. 2571:
Mathematical Methods in Geophysical Imaging III
Siamak Hassanzadeh, Editor(s)

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