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 mac...
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doaj-d92454a9e6fe4429b9ae33ea000e13182021-07-17T04:34:58ZengElsevierUnderground Space2467-96742021-08-0164364378Investigating low-permeability sandstone based on physical experiments and predictive modelingZhiming Chao0Guotao Ma1Kun He2Meng Wang3Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610000, ChinaSchool of Engineering, The University of Warwick, Coventry CV4 7AL, UK; Corresponding author.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610000, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, School of Architecture and Environment, Sichuan University, Chengdu, ChinaAn 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 machine-learning model. The following conclusions are drawn. With an increase in the confinement pressure, the permeability of low-permeability 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 PSO-BPANN 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.http://www.sciencedirect.com/science/article/pii/S2467967420300271Low-permeability sandstonePermeabilityMachine learningSensitivity analysisMoisture saturation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhiming Chao Guotao Ma Kun He Meng Wang |
spellingShingle |
Zhiming Chao Guotao Ma Kun He Meng Wang Investigating low-permeability sandstone based on physical experiments and predictive modeling Underground Space Low-permeability sandstone Permeability Machine learning Sensitivity analysis Moisture saturation |
author_facet |
Zhiming Chao Guotao Ma Kun He Meng Wang |
author_sort |
Zhiming Chao |
title |
Investigating low-permeability sandstone based on physical experiments and predictive modeling |
title_short |
Investigating low-permeability sandstone based on physical experiments and predictive modeling |
title_full |
Investigating low-permeability sandstone based on physical experiments and predictive modeling |
title_fullStr |
Investigating low-permeability sandstone based on physical experiments and predictive modeling |
title_full_unstemmed |
Investigating low-permeability sandstone based on physical experiments and predictive modeling |
title_sort |
investigating low-permeability sandstone based on physical experiments and predictive modeling |
publisher |
Elsevier |
series |
Underground Space |
issn |
2467-9674 |
publishDate |
2021-08-01 |
description |
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 machine-learning model. The following conclusions are drawn. With an increase in the confinement pressure, the permeability of low-permeability 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 PSO-BPANN 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. |
topic |
Low-permeability sandstone Permeability Machine learning Sensitivity analysis Moisture saturation |
url |
http://www.sciencedirect.com/science/article/pii/S2467967420300271 |
work_keys_str_mv |
AT zhimingchao investigatinglowpermeabilitysandstonebasedonphysicalexperimentsandpredictivemodeling AT guotaoma investigatinglowpermeabilitysandstonebasedonphysicalexperimentsandpredictivemodeling AT kunhe investigatinglowpermeabilitysandstonebasedonphysicalexperimentsandpredictivemodeling AT mengwang investigatinglowpermeabilitysandstonebasedonphysicalexperimentsandpredictivemodeling |
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