A new well log interpretation model based on Emergent Self-organizing Maps

When the training samples of well log data for Kohonen Self-Organizing Maps(KSOM) are large and high dimensional, the adjacent clusters may be overlap in a common region. In the paper, a new model of clustering analysis and recognition for well log data is proposed with Ultsch Emergent Self-organizing Maps(ESOM) of neural network. This method can overcome the weakness of KSOM and optimize the result of clustering by using component map, U-Matrix and P-Matrix to visually compare and analysis the clusters on boundless toroid topology grids. This model is trained by the data clustering and visualization for key wells' data in oilfield block. The results show that this new model has good application prospects for well log interpretation using the trained pattern classifier.