Prediction of the shale gas permeability: A data mining approach

Chao, Zhiming; Dang, Yabin; Pan, Y.; Wang, Feiyang; Wang, Meng; Zhang, Jiao; Yang, Chuanxin

doi:10.1016/j.gete.2023.100435

Cited by 31 publications

(8 citation statements)

References 40 publications

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“…The statistics of the input and output parameters of our dataset are shown in Table 1. The input parameters of the neural network have varying dimensions, so it makes sense to normalize the input parameters in order to increase the predicting precision of the neural network [28,29]. The input parameters are mapped to a range from 0 to 1 using the normalization formula below:…”

Section: Matching the Homogenized Parameters To The Heterogeneous Par...mentioning

confidence: 99%

The Data-Driven Homogenization of Mohr–Coulomb Parameters Based on a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN)

Gao,

Huang,

et al. 2023

Applied Sciences

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Homogenization methods can characterize the mechanical properties of these materials based on appropriate constitutive models and data. They are also applied to the characterization of mechanical parameters under complex geotechnical conditions in geotechnical engineering because of the complexity and heterogeneous nature of geotechnical materials. Unfortunately, existing homogenization methods for geotechnical mechanical parameters often incur immense computational costs. Hence, a framework that utilizes finite element analysis for generating a dataset which is then trained using a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN) to obtain the homogenized Mohr–Coulomb parameters of the soils is proposed. This is the first time that Bayesian optimization and a BP-ANN have been used in conjunction to predict the homogenized mechanical parameters of soils. The dataset used for training the data is generated using the commercial FEM software ABAQUS (6.10). The maximum difference between the top and bottom part of the tunnel of the heterogeneous model and homogeneous model of our test cases only varies by 5.3%, thereby verifying the excellence of the Bayesian Optimized BP-ANN.

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Section: Matching the Homogenized Parameters To The Heterogeneous Par...mentioning

confidence: 99%

The Data-Driven Homogenization of Mohr–Coulomb Parameters Based on a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN)

Gao,

Huang,

et al. 2023

Applied Sciences

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“…Permeability, which refers to the capacity of a soil to transmit specific fluids, is a significant indicator of soil settlement. Various factors influence permeability, including soil types, soil structure, and the characteristics of permeating fluids [54,55]. In terms of the soil itself, gradation plays a crucial role in determining the magnitude of the permeability coefficient.…”

Section: Permeabilitymentioning

confidence: 99%

Impacts of Contaminants from Different Sources on Geotechnical Properties of Soils

Zhao,

Zhang,

Zhang

et al. 2023

Sustainability

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Within sites affected by industrial, domestic, and agricultural contaminants, the geotechnical characteristics of soils are susceptible to a certain degree of deterioration. The resultant corrosion of concrete exacerbates the vulnerability of underground structures, posing a potential hazard to the stability of superstructures. However, the current lack of comprehensive understanding regarding the precise influence of contaminants from different sources on the geotechnical properties of soils underscores the critical need for further research in this field. This review aims to elucidate the underlying mechanisms of various impacts, revealing that the permeability, shear strength, and compressibility of soils can either increase or decrease depending on the specific contaminants present. Notably, even though these impacts may not manifest prominently in the short term, their persistence can endure over an extended duration. The primary objective of this comprehensive review is to draw the attention of the scientific community and policy makers to this issue, emphasizing the need to mitigate potential hazards and safeguard a habitable environment for present and future generations.

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“…In practical engineering, most of soil also bears in situ vertical confining pressure that is generated by overlaying soil, buildings, etc (Chao et al, 2022a;Chao et al, 2022b;Chao et al, 2023a;Cui et al, 2022a;Cui et al, 2022b;Liu, 2015;Miao et al, 2019;). Many scholars have pointed out that during drying-wetting cycles, volume change of soil are the main reason for variation in the hydro-mechanical characteristics of soil, especially for clayey soil (Cui et al, 2018;Cui et al,2022;Cui et al, 2023;Cuisinier et al, 2014;Li et al, 2022;Shu et al, 2022;).…”

Section: Introductionmentioning

confidence: 99%

Mechanical behaviour of soil under drying–wetting cycles and vertical confining pressures

Chao

Shi

Fowmes

2024

Environmental Geotechnics

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A system for preparing soil specimens subjected to drying-wetting cycles whilst under vertical confining pressures is introduced with Clayey soil specimens subjected to different drying-wetting histories, then the relative performance tested using consolidated undrained triaxial shear tests. Meanwhile, their soil water retention properties were also measured. The experimental results indicate that drying-wetting cycles lead to a rise in the matric suction for soil with high moisture content, and the decrease of matric suction for soil with low moisture content. Partly owing to the higher pore water pressure, peak shear strength reduces gradually during drying-wetting cycles. The impacts of drying-wetting cycles on hydro-mechanical properties of soil specimens during the first cycle are larger than those during the second and the third cycles as the highest matric suction of soil occurs during the first cycle. Vertical confining pressure is shown to limit the impact of drying-wetting cycles on the hydro-mechanical properties of soil effectively because of its restricting effects on the volumetric deformation of soil during the cycles.

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Prediction of the shale gas permeability: A data mining approach

Cited by 31 publications

References 40 publications

The Data-Driven Homogenization of Mohr–Coulomb Parameters Based on a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN)

The Data-Driven Homogenization of Mohr–Coulomb Parameters Based on a Bayesian Optimized Back Propagation Artificial Neural Network (BP-ANN)

Impacts of Contaminants from Different Sources on Geotechnical Properties of Soils

Mechanical behaviour of soil under drying–wetting cycles and vertical confining pressures

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