Image segmentation and flow prediction of digital rock with U-net network

Computed tomography (CT) images of sandstone contain rich reservoir information. Analyzing digital rock images is important for geological research and the flow in the subsurface. This paper presents a workflow for assessing digital rock petrophysical properties based on machine learning techniques, including 1) automatic segmentation of sandstone rock images using U-net networks, 2) permeability prediction using machine learning, and 3) flow simulation by deep learning. First, using the U-net network, the rock images are binary-segmented into matrix and pore, and multisegmented into the matrix, pore, and mineral. The accuracy and intersection over union (IOU) are used to evaluate the performance of image segmentation. The accuracy and IOU of binary segmentation results are 99.87% and 0.9986, and the results for multi-segmentation are 96.77% and 0.7281, respectively. Then, the key features of CT images influencing sandstone permeability are extracted, and the analysis of image features reveals that the hydraulic radius is the most important parameter for permeability prediction. After that, the sandstone permeability is predicted by long short-term memory (LSTM) and random forest (RF) and then compared with the permeability calculated by the lattice Boltzmann (LBM) method. The mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) are used to quantitatively evaluate the error of permeability prediction. The studies show that the precision of RF in permeability prediction is higher than that of LSTM, and when all the feature parameters are used as input, the accuracy of permeability prediction is a little higher than that when only the hydraulic radius is used as input. Finally, this paper refines a new U-net model to predict the flow velocity field from CT images, and this new U-net model can reduce the computation time by 98.59% compared with the LBM method. This study will be significant for applying deep learning in simulate the flow in digital rock.