About This Item
- Full TextFull Text(subscription required)
- Pay-Per-View PurchasePay-Per-View
Purchase Options Explain
Share This Item
The AAPG/Datapages Combined Publications Database
Journal of Petroleum Geology
Abstract

Journal of Petroleum Geology, Vol.
POROSITY
AND PERMEABILITY PREDICTIONS
* Centre for Petroleum Engineering, The University of New South Wales, NSW 2052 Australia.
** Current Address: West Australian Petroleum Pty. Ltd. GPO Box 51580 WA 6001 Perth Australia.
Abstract
reservoir
characterisation concept to the calculation of petrophysical
properties requires the prediction of lithofacies followed by the
assignment of petrophysical properties according to the specific
lithofacies predicted. Common classification methods which fulfil
this task include discriminant analysis and back-propagation
neural networks. While discriminant analysis is a
well-established statistical classification method
back-propagation neural networks are relatively new and their
performance in
predicting
lithofacies
porosity
and permeability
when compared to discriminant analysis has not been widely
studied. This work compares the performance of these two methods
in prediction of
reservoir
properties by considering log and core
data from a shaly glauconitic
reservoir
.
Pay-Per-View Purchase Options
The article is available through a document delivery service. Explain these Purchase Options.
Protected Document: $10 | |
Internal PDF Document: $14 | |
Open PDF Document: $24 |