A Local Linear Neurofuzzy Model for the Prediction of Permeability from Well-log Data in Carbonate Reservoirs

The prediction of permeability in heterogeneous carbonates from well-log data represents a difficult and complex problem. The authors present a new approach for the prediction of permeability. It is based on the recently developed neurofuzzy interpretation of locally linear models, which has led to the introduction of the intuitive incremental learning algorithm referred to as the locally linear model tree. The incremental learning algorithm initializes the model with an optimal linear least-squares estimate and automatically increases the number of neurons in each epoch. The model is optimized for the number of neurons to avoid overfitting and to provide maximum generalization by considering the error index of validation sets during training. The effectiveness of the methodology is demonstrated with a case study in a heterogeneous carbonate reservoir (Fahliyan formation in southwest Iran). Wireline data and core data sets from three wells that are located in the center of the field provide the data for the learning task. Core permeability and well-log data from a forth well provide the basis for model validation. The numerical results of the neurofuzzy modeling method are compared with the results of multiple linear regression and artificial neural network. The results are very satisfactory and open the possibilities for future research and applications.