ALBERT

Beschreibung: Summary X-ray imaging of porous media has revolutionized the interpretation of various microscale phenomena in subsurface systems. The volumetric images acquired from this technology, known as digital rocks (DR), make it a suitable candidate for machine learning and computer-vision applications. The current routine DR frameworks involving image processing and modeling are susceptible to user bias and expensive computation requirements, especially for large domains. In comparison, the inference with trained machine-learning models can be significantly cheaper and computationally faster. Here we apply two popular convolutional neural network (ConvNet) architectures [residual network (ResNet) and ResNext] to learn the geometry of the pore space in 3D porous media images in a supervised learning scheme for flow-based characterization. The virtual permeability of the images to train the models is computed through a numerical simulation solver. Multiple ResNet variants are then trained to predict the continuous permeability value (regression). Our findings demonstrate the suitability of such networks to characterize volume images without having to resort to further ad-hoc and complex model adjustments. We show that training with richer representation of pore space improves the overall performance. We also compare the performance of the models statistically based on multiple metrics to assess the accuracy of the regression. The model inference of permeability from an unseen sandstone sample is executed on a standard workstation in less than 120 ms/sample and shows a score of 0.87 using explained variance score (EVS) metric, a mean absolute error (MAE) of 0.040 darcies, and 18.9% relative error in predicting the value of permeability compared to values acquired through simulation. Similar metrics are obtained when training with carbonate rock images. The training wall time and hyperparameters setting of the model are discussed. The findings of this study demonstrate the significant potential of machine learning for accurate DR analysis and rock typing while leveraging automation and scalability.