NDLI: The temperature-based localization for the application of EnKF on automatic history matching of the SAGD process

Content Provider	Springer Nature Link
Author	Huang, Yingchao Zeng, Fanhua
Copyright Year	2016
Abstract	The ensemble Kalman filter (EnKF) has been successfully applied to data assimilation in steam-assisted gravity drainage (SAGD) process, but applications of localization for the EnKF in the SAGD process have not been studied. Distance-based localization has been reported to be very efficient for assimilation of large amounts of independent data with a small ensemble in water flooding process, but it is not applicable to the SAGD process, since in the SAGD process, oil is produced mainly from the transition zone steam chamber to cold oil instead of the regions around the producer. As the oil production rate is mainly affected by the temperature distribution in the transition zone, temperature-based localization was proposed for automatic history matching of the SAGD process. The regions of the localization function were determined through sensitivity analysis by using a large ensemble with 1000 members. The sensitivity analysis indicated that the regions of cross-correlations between oil production and state variables are much wider than the correlations between production data and model variables. To choose localization regions that are large enough to include the true regions of non-zero cross-covariance, the localization function is defined based on the regions of non-zero covariances of production data to state variables. The non-zero covariances between production data and state variables are distributed in accordance with the steam chamber. This makes the definition of a universal localization function for different state variables easier. Based on the cross-correlation analysis, the temperature range in which oil production is contributed is determined, and beyond or below this range, the localization function reduces from one, and at the critical temperature or steam temperature, the localization function reduces to zero. The temperature-based localization function was obtained through modifying the distance-based localization function. Localization is applied to covariance of data with permeability, saturation, and temperature, as well as the covariance of data with data. A small ensemble (10 ensemble members) was employed in several case studies. Without localization, the variability in the ensemble collapsed very quickly and lost the ability to assimilate later data. The mean variance of model variables dropped dramatically by 95 %, and there was almost no variability in ensemble forecasts, while the prediction was far from the reference with data mismatch keeping up at a high level. At least 50 ensemble members are needed to keep the qualities of matches and forecasts, which significantly increases the computation time. The EnKF with temperature-based localization is able to avoid the collapse of ensemble variability with a small ensemble (10 members), which saves the computation time and gives better history match and prediction results.
Starting Page	187
Ending Page	212
Page Count	26
File Format	PDF
ISSN	14200597
Journal	Computational Geosciences
Volume Number	20
Issue Number	1
e-ISSN	15731499
Language	English
Publisher	Springer International Publishing
Publisher Date	2016-01-26
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ensemble Kalman filter SAGD Temperature-based localization Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Mathematical Modeling and Industrial Mathematics Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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