Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother
Tóm tắt
Detailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited information. With limited data, the estimation accuracy of traditional geostatistical methods is strongly affected by the parameterization of the prior description of the SZA. To improve characterization performance, we parameterized the DNAPL saturation field using a physics‐based approach. We trained a convolutional variational autoencoder (CVAE) using data from multiphase modeling that captures the physics of DNAPL infiltration. The trained CVAE network was used in SZA inversion to obtain an improved prior DNAPL saturation field, instead of the typical stationary prior covariances. We then integrated the CVAE network into an iterative ensemble smoother (ES), to formulate a joint inversion framework. To overcome difficulties from limited/sparse data, we incorporated hydrogeological and geophysical datasets in the proposed inversion framework. To evaluate the performance of our method, we conducted numerical experiments in a hypothetical heterogeneous aquifer with an intricate SZA. The results show that the CVAE was an effective and efficient parameterization method which can capture the DNAPL infiltration patterns better than a Gaussian prior. The improved prior, combined with multisource datasets, can result in better resolution, and overall improved SZA characterization. In contrast to the standard ES method, the proposed framework reconstructed the SZA more accurately. We also demonstrated that DNAPL depletion behavior and dissolved concentration profiles can be predicted accurately using the estimated SZA.
Từ khóa
Tài liệu tham khảo
Abriola L. M. Christ J. A. Pennell K. D. &Ramsburg C. A.(2012).Source Remediation Challenges. InP. K.Kitanidis &P. L.McCarty(Eds.) Delivery and mixing in the subsurface: Processes and design principles for in situ remediation(pp.239–276).New York NY:Springer.
Archie G. E., 1942, The electrical resistivity log as an aid in determining some reservoir characteristics, Transactions of the American Institute of Mining and Metallurgical Engineers, 146, 54
Bear J., 1972, Dynamics of fluids in porous media
Brooks A. N. &Corey A. T.(1964).Hydraulic properties of porous media. InA. T.Corey R. E.Dils &V. M.Yevdjevich(Eds.) Hydrology papers(pp.1–25).Fort Collins CO:Colorado State University.
Canchumun S. W. A., 2020, Recent developments combining ensemble smoother and deep generative networks for facies history matching
Chan S., 2020, Parametrization of stochastic inputs using generative adversarial networks with application in geology, Frontiers in Water, 2, 10.3389/frwa.2020.00005
Cohen R. M. Mercer J. W. &Matthews J.(1993).Properties of Fluid and Media. InC. K.Smoley(Eds.) DNAPL site evaluation.Boca Raton FL:CRC Press.
C. O. M. S. O. L Multiphysics., 1994, COMSOL Multiphysics
Gerhard J. I., 2003, Relative permeability characteristics necessary for simulating DNAPL infiltration, redistribution, and immobilization in saturated porous media, Water Resources Research, 39, 1213
Goodfellow I., 2016, Deep learning
Goodfellow I. Pouget‐Abadie J. Mirza M. Xu B. Warde‐Farley D. Ozair S. et al. (2014).Generative adversarial nets. InConference on neural information processing systems (NeuIPS)(pp.2672–2680).
Higgins I., 2017, Beta‐vae: Learning basic visual concepts with a constrained variational framework, 6
Hodges R. A., 2001, Numerical modeling of free‐phase TCE/PCE emplacement and transport in the M‐area basin and evaluation of thermal remediation technologies: SCUREF task order west 041 final report, 126
Kingma D. P., 2014, Adam: A method for stochastic optimization
Kingma D. P., 2014, Auto‐encoding variational Bayes
LeCun Y., 1989, Generalization and network design strategies
Linde N. &Doetsch J.(2016).Joint inversion in hydrogeophysics and near‐surface geophysics. InM.Moorkamp P.Lelièvre N.Linde &A.Khan(Eds.) Integrated imaging of the Earth. (ch.7 pp.119–135). Hoboken New Jersey: John Wiley & Sons.https://doi.org/10.1002/9781118929063.ch7
Liu M. &Grana D.(2018).Ensemble‐based seismic history matching with data reparameterization using convolutional autoencoder. In2018 SEG International exposition and Annual Meeting.(5):3156–3160.Anaheim CA:Society of Exploration Geophysicists.
Major D., 2012, Development of a protocol and a screening tool for selection of DNAPL source area remediation: Naval facilities engineering command port Hueneme CA
National Research Council 2005 National research Council Washington DC
National Research Council 2013 National research Council Washington DC
Pankow J. F., 1996, Dense chlorinated solvents and other DNAPLs in groundwater: History, behavior, and remediation
Pruess K., 2002, TMVOC, a numerical simulator for three‐phase non‐isothermal flows of multicomponent hydrocarbon mixtures in saturated‐unsaturated heterogeneous media
Sneed M., 2001, Hydraulic and mechanical properties affecting groundwater flow and aquifer system compaction, San Joaquin Valley, California, 26
Ioffe S., 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 448