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Title: Advanced Algorithms for Seismic Modeling Applications.

Abstract

The accuracy of an artifical Neural network (ANN) algorithm is a crucial issue in the estimation of an oil field's reservoir properties from the log and seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN's accuracy statistic from a finite sample set.

Authors:
; ; ;
Publication Date:
Research Org.:
Oak Ridge Operations, Oak Ridge, TN
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EE)
OSTI Identifier:
774087
Report Number(s):
VOL 26; ISSUE 2000; SERIAL #369875
Journal ID: 369875
DOE Contract Number:  
AI05-95OR22418
Resource Type:
Journal Article
Journal Name:
Reservoir parameter estimation using a hybrid neural network
Additional Journal Information:
Journal Volume: 26; Journal Issue: 2000
Country of Publication:
United States
Language:
English
Subject:
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; 07 ISOTOPES AND RADIATION SOURCES

Citation Formats

F. Aminzadeh, Jacob Barhen, C.W. Glover, and N.B. Toomarian. Advanced Algorithms for Seismic Modeling Applications.. United States: N. p., 1999. Web.
F. Aminzadeh, Jacob Barhen, C.W. Glover, & N.B. Toomarian. Advanced Algorithms for Seismic Modeling Applications.. United States.
F. Aminzadeh, Jacob Barhen, C.W. Glover, and N.B. Toomarian. Thu . "Advanced Algorithms for Seismic Modeling Applications.". United States.
@article{osti_774087,
title = {Advanced Algorithms for Seismic Modeling Applications.},
author = {F. Aminzadeh and Jacob Barhen and C.W. Glover and N.B. Toomarian},
abstractNote = {The accuracy of an artifical Neural network (ANN) algorithm is a crucial issue in the estimation of an oil field's reservoir properties from the log and seismic data. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bounds on an ANN's accuracy statistic from a finite sample set.},
doi = {},
journal = {Reservoir parameter estimation using a hybrid neural network},
number = 2000,
volume = 26,
place = {United States},
year = {1999},
month = {9}
}