Recent developments combining ensemble smoother and deep generative networks for facies history matching
From MaRDI portal
Publication:2225361
DOI10.1007/s10596-020-10015-0zbMath1453.86038arXiv2005.10638OpenAlexW3106811345MaRDI QIDQ2225361
Smith W. A. Canchumuni, Jose D. B. Castro, Júlia Potratz, Marco Aurélio C. Pacheco, Alexandre Anozé Emerick
Publication date: 8 February 2021
Published in: Computational Geosciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.10638
Related Items
Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification ⋮ Bridging deep convolutional autoencoders and ensemble smoothers for improved estimation of channelized reservoirs ⋮ Data assimilation predictive GAN (DA-PredGAN) applied to a spatio-temporal compartmental model in epidemiology
Uses Software
Cites Work
- Unnamed Item
- A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models
- Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics
- History matching with an ensemble Kalman filter and discrete cosine parameterization
- Eigenvalue computation in the 20th century
- A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)
- Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation
- Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
- Calibration of imperfect models to biased observations
- Analysis of iterative ensemble smoothers for solving inverse problems
- Conditional simulation of complex geological structures using multiple-point statistics
- Parametric generation of conditional geological realizations using generative neural networks
- Stochastic seismic waveform inversion using generative adversarial networks as a geological prior
- A deep-learning-based geological parameterization for history matching complex models
- Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
- History matching of facies distribution with the EnKF and level set parameterization
