A modular signal processing model for permeability prediction in petroleum reservoir

The use of the artificial neural network (ANN) especially the backpropagation neural network (BPNN) has been a promising tool for well log analysis in predicting permeability. However, due to the range of permeability data, it is normally converted using a logarithmic transform before being used for data analysis by the BPNN. This has an impact on the accuracy of permeability prediction. This paper suggests a model for improving the permeability prediction. It first divides the whole sample space of the permeability values according to their logarithmic region, and then generates individual BPNNs for each logarithmic region. In this initial study, learning vector quantisation (LVQ) is used for this purpose for separating the data. After that, each region is then handled by each BPNN. This method not only preserves the resolution of the permeability, but at the same time, increases the prediction accuracy. The contributions of this paper are to identify the problems in the signal processing of permeability prediction, and exploit a new direction of improving permeability prediction using well logs.