Print this page
Click to view page image in pdf format.
7th Middle East Geosciences Conference and Exhibition
Manama, Bahrain
March 27-29, 2006
Seismic
Data Using Abductive Networks
1 Applied Electrical Engineering, KFUPM, Hail Community Coleege, KFUPM, Hail, Saudi Arabia, phone:
0564203171, [email protected]
2 Computer Engineering Dept, KFUPM, KFYPM, Dhahran, 31261, Saudi Arabia
3 ARAMCO, Dhahran, Saudi Arabia
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.