Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A gas migration law study of a large-scale 3D physical similarity simulation with an adaptive Kalman filter algorithm

A gas migration law study of a large-scale 3D physical similarity simulation with an adaptive... In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law.Design/methodology/approachFirst, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend.FindingsThis approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach.Originality/valueFor the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Assembly Automation Emerald Publishing

A gas migration law study of a large-scale 3D physical similarity simulation with an adaptive Kalman filter algorithm

Hao, Yuyu; Li, Shugang; Zhang, Tianjun
Assembly Automation , Volume 42 (1): 8 – Jan 11, 2022
Download PDF
8 pages

Loading next page...
 
/lp/emerald-publishing/a-gas-migration-law-study-of-a-large-scale-3d-physical-similarity-wpy30N4ZAI
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0144-5154
DOI
10.1108/aa-06-2021-0084
Publisher site
See Article on Publisher Site

Abstract

In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law.Design/methodology/approachFirst, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend.FindingsThis approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach.Originality/valueFor the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

Journal

Assembly Automation – Emerald Publishing

Published: Jan 11, 2022

Keywords: Fick’s law; Diffusion flux; Kalman filter; Gas delivery; Physical similarity simulation

References

  • Gas migration of coal in front of a mining face considering crack evolution
    Cao, S.G.; Luo, F.; Liu, Y.B.; Li, Y.; Guo, P.; Li, G.D.
  • Study on deformation and energy release characteristics of overlying strata under different mining sequence in close coal seam group based on similar material simulation
    Cui, F.; Jia, C.; Lai, X.
  • Three-dimensional physical similarity simulation of the deformation and failure of a gas extraction surface well in a mining area
    Fu, J.H.
  • Variational Bayesian adaptation of noise covariances in non-linear Kalman filtering
    Hartikainen, S.
  • A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices
    Huang, Y.; Zhang, Y.; Wu, Z.; Ning, L.; Chambers, J.
  • Investigation on multi-scale pore seepage model of shale gas reservoir considering diffusion and slippage effect
    Jiang, L.J.; Sun, H.G.; Yang, S.; Zhang, Y.; Xu, H.
  • Numerical modeling and experimental validation of anomalous time and space subdiffusion for gas transport in porous coal matrix
    Kang, J.H.; Zhou, F.B.; Xia, T.Q.
  • Study on mechanism of influence of mining speed on roof movement based on microseismic monitoring
    Ke, M.; Wang, S.J.; Yuan, F.Z.; Peng, Y.L.; Jia, S.M.; Gong, F.Q.
  • Research and development of 3D large-scale physical simulation experimental system for coal and gas co-extraction and its application
    Li, S.G.; Wei, Z.Y.; Lin, H.F.; Zhao, P.X.; Xiao, P.; Hao, Y.Y.
  • Gas adsorption characteristics of similar materials with coal and gas couple of coal and gas
    Li, S.G.; Zhao, B.; Zhao, P.X.; Yang, E.H.; Xu, P.Y.
  • Study of gas adsorption characteristics of coal-like materials with influence factors
    Li, S.G.; Zhao, B.; Zhao, P.X.; Lin, H.F.; Ding, Y.
  • Fractional Central difference Kalman filter with unknown prior information
    Liu, T.; Cheng, S.; Wei, Y.; Li, A.; Wang, Y.
  • Studies of stress and displacement distribution and the evolution law during rock failure process based on acoustic emission and microseismic monitoring
    Liu, J.P.; Wang, R.; Lei, G.; Si, Y.T.; Xu, S.D.; Li, Y.H.
  • Approaches to adaptive filtering
    Mehra, R.K.
  • Experimental observations of heterogeneous strains inside a dual porosity sample under the influence of gas-sorption: a case study of fractured coal
    Shi, R.; Liu, J.S.; Wang, X.M.; Elsworth, D.
  • Numerical study on movement of dynamic strata in combined open-pit and underground mining based on similar material simulation experiment
    Zhang, J.; Wang, Z.; Song, Z.
  • Processing of building subsidence monitoring data based on fusion Kalman filtering algorithm
    Zhang, J.; Liu, H.; Sun, X.; Liu, S.
  • Fracture analysis of multi-hard roofs based on microseismic monitoring and control techniques for induced rock burst: a case study
    Zhang, W.; Qu, X.; Li, C.; Xu, X.; Wang, Y.
  • Experimental and numerical analyses of apparent gas diffusion coefficient in gas shales
    Zhong, Y.; She, J.; Zhang, H.; Kuru, E.; Yang, B.; Kuang, J.