Estimation of Relative Permeability by Assisted History Matching Using the Ensemble Kalman Filter Method

2009 ◽  
Author(s):  
H. Li ◽  
S. Chen ◽  
D. Yang ◽  
P. Tontiwachwuthikul
Keyword(s):  
Kalman Filter ◽  
Relative Permeability ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Filter Method ◽  
Assisted History Matching ◽  
Kalman Filter Method

scholarly journals Solution Estimation of Logistic Growth Model with Ensemble Kalman Filter Method

2014 ◽  
Vol 14 (2) ◽  
pp. 85
Author(s):  
Vianda Nuning Fitriani ◽  
Kosala Dwidja Purnomo
Keyword(s):  
Kalman Filter ◽  
Analytical Solution ◽  
Ensemble Kalman Filter ◽  
Nonlinear Models ◽  
Specific Form ◽  
Filter Method ◽  
Logistic Growth ◽  
Parabolic Form ◽  
Logistic Growth Model ◽  
Kalman Filter Method

Ensemble Kalman Filter (EnKF) can be applied for linear or nonlinear models. This paper is aimed to estimate the logistic growth of population models using EnKF. The estimation will be compared with the analytical solution. We assume that we can find the analytical solution of the models. The models is in the specific form i.e comparison between the population growth rate and the amount of population is in the parabolic form. The good estimation will be attained by choosing 100 as size of ensembles in EnKF. The result of estimation really so closed to the analytical solution. Keywords : Analytical solution, EnKF, ensemble

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.

Real-time forecasting of fire in a two-story building using ensemble Kalman filter method

2018 ◽  
Vol 97 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Ji ◽  
Chunxiang Liu ◽  
Zihe Gao ◽  
Liangzhu (Leon) Wang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Filter Method ◽  
Kalman Filter Method ◽  
Story Building
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