Reliable and comprehensive well log data is essential for a reasonable evaluation of the underground rock formations and the in-situ hydrocarbons. This log data has to be interpreted with good statistical confidence to predict the performance of a well. In order to automate the process of log interpretation and hence potentially reduce its cost, a Neural Network (NN) can be trained for interpreting logs with reasonable accuracy. This paper discusses the possibility of applying the Simulated Neural Network (SNN) technology to the art and science of well log interpretation.

The algorithm used to generate the SNN used the Kohonen unsupervised learning technique. Basic principles of the SNN thus created have been considered and the results obtained from the training and SNN log interpretation have been discussed.

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