A seismic reservoir permeability prediction approach based on gaussian process machine learning

Predicting permeability using seismic attributes is challenging and fraught with great uncertainty. The inverse relationship, which acts as a bridge between permeability and seismic attributes, needs to be established accurately. Machine learning, especially Gaussian Process (GP), provides a new potential. However, its application to permeability prediction is influenced by the quantity and quality of input features. This study proposes an integrated approach for permeability prediction based on GP. The approach automatically generates 222 extended features from three elastic attributes for GP training. In addition, the synthetic minority oversampling technique is used to overcome the problem of training with imbalanced samples. With the help of feature importance measures based on Shapley values, the features that are important for permeability prediction can be preferentially selected. Validation on a dolomite reservoir in Western China illustrates the role of our approach in enhancing the performance of GP in permeability prediction.