Methods of Artificial Intelligence like Back-Propagation Neural Networks (BPNN) have become popular software tools to predict permeability and porosity from well logs during the last several years. Similar to Multiple-Linear Regression models, Back- Propagation Neural Networks are trained with a set of target values from core measurements. The Self-Organizing Map (SOM) Neural Network method applies an unsupervised training algorithm. Until now this approach has mainly been applied for clustering purposes only, not for predicting reservoir properties. In a new application, SOM technology has been merged with statistical prediction methods to derive the following types of information from well logs and core measurements in one step: 1) Synthetic lithofacies system (clustering) 2) Porosity and permeability (prediction) SOM technology also provides a data visualization tool which allows evaluating relationships between input variables (well logs) and output variables (reservoir properties). SOM models can also be combined with BPNN in order to subdivide the entire set of well log patterns into different lithofacies and run individual BPNN models for each facies. Application of this method showed increase in prediction accuracy and significant timesavings. This paper should be viewed and printed in color.
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