Neural network prospect in reservoir characterization

The precision of an artificial neural network (ANN) algorithm is a key issue in the estimation of an oil 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 ANN's accuracy statistic from a finite sample set. In addition, we also show that an ANN's classification accuracy is dramatically improved by transforming the ANN's 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 ANN's 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 precision 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.

Keywords:

Neural networks , Oil exploration , Reservoir characterization , Seismic attributes