Investigating low-permeability sandstone based on physical experiments and predictive modeling

An innovative method is proposed for preparing low-permeability sandstone with different moisture saturation.The permeability of the prepared low-permeability sandstone sample is measured under different confinement and seepage pressures.Based on the experimental results,10 types of different machine-learning models combined with optimization algorithms are established to predict the permeability of low-permeability sandstone.A comprehensive evaluation and comparison of the 10 types of machine-learning models are conducted to identify the machine-learning model with the best performance.Next,a sensitivity analysis is conducted on the factors influencing the permeability of low-permeability sandstone to elucidate the internal mechanism according to the established machinelearning model.The following conclusions are drawn.With an increase in the confinement pressure,the permeability of lowpermeability sandstone with different moisture-saturation levels decreases,and the permeability of low-permeability sandstone decreases with an increase in the moisture saturation.The hybrid particle swarm optimization algorithm-backpropagation artificial neural network(PSO-BPANN)model provides the best results for predicting the permeability of low-permeability sandstone.The established PSOBPANN model is also reliable for predicting the permeability of low-permeability sandstone from other engineering sites.Among the influencing factors,moisture saturation has the largest effect on the permeability of low-permeability sandstone,followed by the confinement pressure.