利用机器学习模型预测多孔介质的渗透率是当前孔隙尺度模型的关键研究方向之一。由于三维多孔介质数据无法直接应用于经典机器学习模型，对孔隙空间结构进行特征提取是有必要的。深度学习模型作为经典机器学习模型的进阶，在多孔介质三维数字图像预测渗透率方面取得许多成功，但模型的计算成本相当高。该研究提取多孔介质切片的孔隙结构特征，将数字图像转化为多维向量并作为机器学习模型的输入，在减少数据输入量、大幅度提高训练效率的同时，模型保持了出色的预测能力，其中长短期记忆神经网络（LSTM）的预测结果最佳。

Using machine learning models to predict the permeability of porous media is one of the key research directions of current pore-scale models. Since three-dimensional porous media data cannot be directly applied to classical machine learning models, it is necessary to perform feature extraction on the pore s pace structure. Deep learning, as the advancement of classical machine learning models, has achieved many successes in predicting permeability from three-dimensional digital images of porous media, but the computational cost is quite high. This study extracted pore structure features of slices of porous media, converting digital images into multidimensional vectors and serving as input to machine learning models. While reducing the amount of input data and greatly improving the training efficiency, the models maintained excellent prediction performance, where the long short -term memory (LSTM) neural network achieved the best results.