Surrogate model based iterative ensemble smoother for subsurface flow data assimilation

2017 ◽  
Vol 100 ◽  
pp. 96-108 ◽  
Author(s):  
Haibin Chang ◽  
Qinzhuo Liao ◽  
Dongxiao Zhang
Keyword(s):  
Data Assimilation ◽  
Surrogate Model ◽  
Subsurface Flow ◽  
Flow Data ◽  
Model Based ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

Data assimilation of subsurface flow via iterative ensemble smoother and physics-informed neural network

2020 ◽  
Author(s):  
Nanzhe Wang ◽  
Haibin Chang
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Surrogate Model ◽  
Flow Problem ◽  
Subsurface Flow ◽  
High Accuracy ◽  
Training Data ◽  
Model Parameters ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

<p>Subsurface flow problems usually involve some degree of uncertainty. For reducing the uncertainty of subsurface flow prediction, data assimilation is usually necessary. Data assimilation is time consuming. In order to improve the efficiency of data assimilation, surrogate model of subsurface flow problem may be utilized. In this work, a physics-informed neural network (PINN) based surrogate model is proposed for subsurface flow with uncertain model parameters. Training data generated by solving stochastic partial differential equations (SPDEs) are utilized to train the neural network. Besides the data mismatch term, the term that incorporates physics laws is added in the loss function. The trained neural network can predict the solutions of the subsurface flow problem with new stochastic parameters, which can serve as a surrogate for approximating the relationship between model output and model input. By incorporating physics laws, PINN can achieve high accuracy. Then an iterative ensemble smoother (ES) is introduced to implement the data assimilation task based on the PINN surrogate. Several subsurface flow cases are designed to test the performance of the proposed paradigm. The results show that the PINN surrogate can significantly improve the efficiency of data assimilation task while maintaining a high accuracy.</p>

Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

2016 ◽  
Vol 20 (5) ◽  
pp. 929-952 ◽  
Author(s):  
Mohammadali Tarrahi ◽  
Siavash Hakim Elahi ◽  
Behnam Jafarpour
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Subsurface Flow ◽  
Flow Data

scholarly journals Revising the stochastic iterative ensemble smoother

2019 ◽  
Author(s):  
Patrick N. Raanes ◽  
Andreas S. Stordal ◽  
Geir Evensen
Keyword(s):  
Data Assimilation ◽  
History Matching ◽  
Kalman Smoother ◽  
Multiple Data ◽  
Current Formulation ◽  
Ensemble Smoother ◽  
Careful Treatment ◽  
Randomized Maximum Likelihood ◽  
Iterative Ensemble Smoother ◽  
Lorenz 96

Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm, without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For example, it is shown (a) how ensemble linearizations relate to average sensitivity, and (b) that the ensemble does not loose rank during updates. The paper also draws significantly on the theory of the (deterministic) iterative ensemble Kalman smoother (IEnKS). Comparative benchmarks are obtained with the Lorenz-96 model with these two smoothers and the ensemble smoother using multiple data assimilation (ES-MDA).

An adaptive Gaussian process-based iterative ensemble smoother for data assimilation

2018 ◽  
Vol 115 ◽  
pp. 125-135 ◽  
Author(s):  
Lei Ju ◽  
Jiangjiang Zhang ◽  
Long Meng ◽  
Laosheng Wu ◽  
Lingzao Zeng
Keyword(s):  
Data Assimilation ◽  
Gaussian Process ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

scholarly journals Revising the stochastic iterative ensemble smoother

2019 ◽  
Vol 26 (3) ◽  
pp. 325-338 ◽  
Author(s):  
Patrick Nima Raanes ◽  
Andreas Størksen Stordal ◽  
Geir Evensen
Keyword(s):  
Data Assimilation ◽  
History Matching ◽  
Kalman Smoother ◽  
Multiple Data ◽  
Current Formulation ◽  
Ensemble Smoother ◽  
Careful Treatment ◽  
Randomized Maximum Likelihood ◽  
Iterative Ensemble Smoother ◽  
Lorenz 96

Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For example, it is shown (a) how ensemble linearizations relate to average sensitivity and (b) that the ensemble does not lose rank during updates. The paper also draws significantly on the theory of the (deterministic) iterative ensemble Kalman smoother (IEnKS). Comparative benchmarks are obtained with the Lorenz 96 model with these two smoothers and the ensemble smoother using multiple data assimilation (ES-MDA).

Sign in / Sign up

Export Citation Format

Share Document