scholarly journals Relative Permeability Estimation by Ensemble Kalman Filter Using Function Transformation

2009 ◽  
Vol 52 (5) ◽  
pp. 248-256
Author(s):  
Takashi Goda ◽  
Kozo Sato
Keyword(s):  
Kalman Filter ◽  
Relative Permeability ◽  
Ensemble Kalman Filter ◽  
Permeability Estimation ◽  
Function Transformation

Simultaneous Estimation of Relative Permeability and Capillary Pressure for PUNQ-S3 Model With a Damped Iterative-Ensemble-Kalman-Filter Technique

SPE Journal ◽  
2017 ◽  
Vol 22 (03) ◽  
pp. 971-984 ◽  
Author(s):  
Yin Zhang ◽  
Zhaoqi Fan ◽  
Daoyong Yang ◽  
Heng Li ◽  
Shirish Patil
Keyword(s):  
Kalman Filter ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Simultaneous Estimation ◽  
Reservoir Models ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother ◽  
Capillary Pressure Curves

Summary A damped iterative-ensemble-Kalman-filter (IEnKF) algorithm has been proposed to estimate relative permeability and capillary pressure curves simultaneously for the PUNQ-S3 model, while its performance has been compared with that of the CMOST module, iterative-ensemble-smoother (IES) algorithm, and traditional ensemble-Kalman-filter (EnKF) technique. The power-law model is used to represent the relative permeability and capillary pressure curves, while three-phase relative permeability for oil phase is determined by use of the modified Stone II model. By assimilating the observed production data, the relative permeability and capillary pressure curves are inversely, automatically, and successively updated, achieving an excellent agreement with the reference cases. Not only are the associated uncertainties reduced significantly during the updating process, but also each of the updated reservoir models predicts the production profile that is in good agreement with the reference cases. Although the damped IEnKF technique shows the highest accuracy on estimation results, history-matching results, and prediction performance for the PUNQ-S3 model, its computational expense is still high compared with the other three techniques. In addition, the variations in the ensemble of the updated reservoir models and production profiles of the damped IEnKF provide a robust and consistent framework for uncertainty analysis.

Ensemble Kalman Filter for Out of Sequence Measurement Tracking

2012 ◽  
Vol 132 (10) ◽  
pp. 1617-1625
Author(s):  
Sirichai Pornsarayouth ◽  
Masaki Yamakita
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

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