Journal of Jilin University(Earth Science Edition) ›› 2017, Vol. 47 ›› Issue (3): 848-859.doi: 10.13278/j.cnki.jjuese.201703205

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Inverse Multiphase Flow Simulation Using Ensemble Kalman Filter: Application to a 2D Sandbox Experiment of DNAPL Migration

Kang Xueyuan1,2, Shi Xiaoqing1, Shi Liangsheng2, Wu Jichun1   

  1. 1. Key Laboratory of Surficial Geochemistry, School of Earth Science and Engineering, Nanjing University,Nanjing 210023, China;
    2. State Key Laboratory of Water Resource & Hydropower Engineering Science, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China
  • Received:2016-08-27 Online:2017-05-26 Published:2017-05-26
  • Supported by:
    Supported by National Natural Science Foundation of China (U1503282, 41672229, 41172206)

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Abstract: The migration behavior and distribution of dense non-aqueous phase liquid(DNAPL)in subsurface are greatly influenced by geological heterogeneity. To fully understand the DNAPL source-zone architecture, parameter estimation is needed to characterize the permeability heterogeneity in multiphase flow simulation. The data assimilation method based on ensemble Kalman filter (EnKF) is applied to solve this parameter estimation problem. The performances of EnKF are investigated and compared by applying EnKF to a real-world and a synthetic DNAPL infiltration experiment in a two-dimensional laboratory-scale sandbox. The factors to control the performance of data assimilation in multiphase flow are also discussed. The results showed that the EnKF method can effectively estimate multiphase model parameters via DNAPL saturation observations. With the increased sampling density in spatial and time scale, EnKF exhibits a promotion of computational accuracy. Especially, EnKF method can produce satisfactory estimation when increasing the spatial sampling density in the high DNAPL-saturation region.

Key words: dense non-aqueous phase liquid, heterogeneity, permeability, ensemble Kalman filter, parameter estimation

CLC Number: 

  • P641.69
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