Rock porosity and permeability are the main factors affecting fluid storage and flow capacity in reservoirs. At present,pore scale modeling and numerical simulation are usually adopted in the estimation of properties of digital cores where modeling is complex and time-consuming. Therefore, based on the CT scanning results of natural cores, 654 sets of training samples were generated using OpenFOAM, and a fast prediction model was established by a machine learning algorithm. Sensitivity analysis was further conducted for model hyperparameters. When the learning rate is 0.003, the model displays strong generalization ability and prediction accuracy is above 90%. The time of prediction is reduced from more than one hour to less than one second.We propose a high efficiency and high-precision pore permeability prediction method of 3D digital cores based on machine learning, which can effectively reduce cost and improve work efficiency.
machine learning; digital cores; permeability prediction; CT scanning
WANG Yicheng, JIANG Hanqiao, YU Fuwei, CHENG Baoyang, XU Fei, LI Junjian. Researches on the pore permeability prediction method of 3D digital cores based on machine learning. Petroleum Science Bulletin, 2019, 04: 354-363.