Estimation of reservoir parameter using a hybrid neural network

Estimation of an oil fieldʹs reservoir properties using seismic data is a crucial issue. The accuracy of those estimates and the associated uncertainty are also important information. This paper demonstrates the use of the k-fold cross validation technique to obtain confidence bound on an Artificial Neural Networkʹs (ANN) accuracy statistic from a finite sample set. In addition, we also show that an ANNʹs classification accuracy is dramatically improved by transforming the ANNʹs 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 ANNʹs convergence time and accuracy are imporved because the ANN must merely find nonlinear perturbations to the starting linear decision boundaries. These technique 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.