The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes
Date
2004
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In this dissertation, I develop a number of new ideas for the statistical determination of reservoir parameters using seismic attributes, combining the classical techniques of multivariate statistics and the more recent methods of neural network analysis. I apply these techniques to both full seismic volumes and maps derived from intervals averaged through these volumes, using the Blackfoot dataset from central Alberta. In this dissertation I show that multilinear regression often provides too simple a solution to the parameter estimation problem, but that the traditional feedforward neural network often provides a solution that is overly complex. My proposed solution is to use radial basis function neural networks for the prediction of reservoir parameters, since this approach combines the power of the multilinear regression technique with the nonlinearity of neural networks. This conclusion is illustrated using both a model dataset that involves an A VO classification problem and the Blackfoot dataset that was mentioned earlier. In this dissertation, several new ideas are presented. First, I derive an improved regression formula for the prediction of S-wave sonic logs from combinations of other logs. Second, I apply a new approach to data clustering, called Mahalanobis clustering, to the interpretation of A VO crossplots and to the extraction of optimal clusters for the radial basis function neural network with centres. Finally, I develop a new approach that combines geostatistics with multiattribute transforms. This technique uses multivariate statistics and neural networks to improve the secondary dataset used in the collocated cokriging technique.
Description
Bibliography: p. 317-323
Some pages are in colour.
Some pages are in colour.
Keywords
Citation
Russell, B. H. (2004). The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24453