Reservoir parameter estimation using a hybrid neural network
The accuracy of an artificial neural network (ANN) algorithm is a crucial issue in the estimation of an oil fields 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 ANNs accuracy statistic from a finite sample set. In addition, we also show that an ANNs classification accuracy is dramatically improved by transforming the ANNs input feature space to a dimensionally smaller, new input space. The new input space represents a feature space that maximizes the linear separation between classes. Thus, the ANNs convergence time and accuracy are improved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These techniques for estimating ANN accuracy bounds and feature space transformations are demonstrated on the problem of estimating the sand thickness in an oil field reservoir based only on remotely sensed seismic data.
neural networks oil exploration reservoir characterization seismic attributes