Permeability estimation on raw micro-CT of carbonate rock samples using deep learning

Abstract

Petrophysical characterization of carbonate reservoirs remains a challenge. The widespread use of X-ray computed microtomography (micro-CT) images in the classical reservoir characterization workflow allows the use of recent artificial intelligence algorithms to improve the process. This work presents an end-to-end workflow for permeability prediction using deep learning models and micro-CT images. A dataset of 37,600 slices from 376 plug samples from Brazilian presalt carbonate rock, along with the laboratory determined absolute permeability of each sample, were used for model training. Three models were tested: two convolutional neural network models (CNN and CNNSPP) and an ImageNet pretrained model (Densenet161). The models were trained using MSE or the Huber loss and with/without data augmentation. All experiments were performed using 10-fold cross-validation, and the models performance were evaluated by the average prediction of all slices for each sample. In this study, the Densenet161 model achieved the best results. The comparison with other models shows that pretrained models have less influence of data augmentation and almost no difference with respect to the loss function. This shows the effect of transfer learning, even if micro-CT images are very different from ImageNet. The results show that the proposed workflow can automate and speed up the characterization of Brazilian presalt carbonate samples by processing micro-CT slices thereby allowing accurate estimations of absolute permeability within a few seconds.