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Modular artificial neural network for prediction of petrophysical properties from well log data

Fung, C.C., Wong, K.W. and Eren, H. (1997) Modular artificial neural network for prediction of petrophysical properties from well log data. IEEE Transactions on Instrumentation and Measurement, 46 (6). pp. 1295-1299.

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Abstract

An application of Kohonen's self-organizing map (SOM), learning-vector quantization (LVQ) algorithms, and commonly used backpropagation neural network (BPNN) to predict petrophysical properties obtained from well-log data are presented. A modular, artificial neural network (ANN) comprising a complex network made up from a number of subnetworks is introduced. In this approach, the SOM algorithm is applied first to classify the well-log data into a predefined number of classes, This gives an indication of the lithology in the well. The classes obtained from SOM are then appended back to the training input logs for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input logs. A set of BPNN that corresponds to different classes is then trained. Once the network is trained, it is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using the proposed method have shown to be fast and accurate as compared to a single BPNN network.

Publication Type: Journal Article
Publisher: IEEE
Copyright: © 1997 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/15775
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