The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic m...The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning.Porous media structures were generated using the Quartet Structure Generation Set(QSGS)method,and a mesoscopic-scale seepage calculation model was applied to compute flow rates.These results were then used to train a deep learning model for rapid prediction.The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow rates,while pressure drops significantly at the throats of fine pores.The trained model predicts seepage mass flow rates with deviations within±20%,achieving a root mean square error of 0.24261 and an average deviation of-0.02197.Importantly,the method performs well even with limited training data,though image-based deep learning approaches may yield better accuracy when larger datasets are available.展开更多
基金Dynamics of CO_(2) Leakage and Seepage in Wellbores Under Reservoir Stimulation,grant number YJCCUS25SFW0004.
文摘The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning.Porous media structures were generated using the Quartet Structure Generation Set(QSGS)method,and a mesoscopic-scale seepage calculation model was applied to compute flow rates.These results were then used to train a deep learning model for rapid prediction.The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow rates,while pressure drops significantly at the throats of fine pores.The trained model predicts seepage mass flow rates with deviations within±20%,achieving a root mean square error of 0.24261 and an average deviation of-0.02197.Importantly,the method performs well even with limited training data,though image-based deep learning approaches may yield better accuracy when larger datasets are available.
