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covariance-localization
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Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding
, Article Advances in Water Resources ; Vol. 69, issue , 2014 , p. 181-196 ; Pishvaie, M. R ; Boozarjomehry, R. B ; Sharif University of Technology
Abstract
Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered...
Real-time oil Reservoir Characterization by Assimilation of Production Data
, Ph.D. Dissertation Sharif University of Technology ; Pishvaie, Mahmoud Reza (Supervisor) ; Bozorgmehry Boozarjomehry, Ramin (Supervisor)
Abstract
Hydrocarbon reservoirs development and management is based on their dynamic models. To encounter various types of error during model building, model parameters are adjusted to produce reservoir historical data by assimilation (history matching) of reservoir production or 4D seismic data. Among the existing sequential methods for automatic history matching, ensemble Kalman filter and its variants have displayed promising results. The innovations of this thesis for ensemble Kalman filter (EnKF) are presented into three major orients; these includes adaptive localization/regularization, characterization of original PUNQ test model and characterization of channelized reservoir.
To mitigate...
To mitigate...