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AAPG Bulletin, Vol. 90 (2006), Program Abstracts (Digital)
7th Middle East Geosciences Conference and Exhibition
Manama, Bahrain
March 27-29, 2006
ABSTRACT: Predicting Log Properties from
Seismic
Data Using Abductive Networks
Osama A. Ahmed1, Radwan Abdel-Aal2, and Husam AlMustafa3
1 Applied Electrical Engineering, KFUPM, Hail Community Coleege, KFUPM, Hail, Saudi Arabia, phone:
0564203171, osamaa@kfupm.edu.sa
2 Computer Engineering Dept, KFUPM, KFYPM, Dhahran, 31261, Saudi Arabia
3 ARAMCO, Dhahran, Saudi Arabia
In this study, abductive network is used to predict reservoir log properties from
seismic
attributes
. Statistical approaches
have been used to model the relationship between the
seismic
data and the reservoir parameters. The idea of using
multiple
seismic
attributes
to predict log properties has been widely used and several case histories have been reported in
the literature using multi-linear stepwise regression and neural networks. The input to any statistical method is a series of
attributes
extracted from the
seismic
data. There is, however, a huge number of
attributes
that can be extracted form the
seismic
dataý. Therefore, an efficient subset of this
attributes
has to be selected before prediction. Exhaustive search of all
attribute combinations is computationally infeasible. As a solution, linear stepwise regression has been proposed which is
based on linear relationships between attribute combinations and log data. Therefore it is suitable for linear regression. For
non linear regression such as neural networks an attribute selection method that embodies the nonlinearity between
attribute combinations and log data is desirable. Abductive Networks should in many ways help in this regard: 1. Abductive
Networks can automatically select a statistically representative subset of optimum predictors from the available set of
seismic
attributes
. 2. Abductive Networks are nonlinear predictors which are proven to outperform linear predictors ý. 3.
Unlike various neural network paradigms, Abductive Networks can provide a closed form analytical relationship between the
selected
seismic
attributes
and the modeled parameter; this can help in fully understanding the geographical structure of
the area.
Copyright © 2006. The American Association of Petroleum Geologists. All Rights Reserved.