The importance of localization in the assimilation of 4D seismic data in the data assimilation process using the EnKF
conference paper
The Ensemble Kaiman Filter (EnKF) is considered a fast and efficient algorithm in the data assimilation process to estimate reservoir properties from measured data. 4D seismics is an important source of information for the reservoir monitoring and the improvement of the geological model. The use of low frequencies for deep surface seismic makes it very complicated to discriminate and estimate properties for fine-grid reservoir models. In this paper it is demonstrated that using vertically averaged seismic data, inverted as time-lapse differences in pore pressure and saturation, greatly improves the quality of the history match and the estimation of the reservoir state. The EnKF may present some problems when assimilating large amounts of data (frequent 4D seismic), as the flexibility of the model solution is strongly reduced. The conditioning of the covariance matrix in the Kaiman gain is a key to avoid the filter divergence. In this study the localization criterion is based on the mere distance or on the streamlines trajectories. Results from 2D and 3D synthetic examples show the importance of localization to ensure the correct functioning of the filter. © 2009 Society of Exploration Geophysicists.
TNO Identifier
409834
ISSN
10523812
Article nr.
No.: SEGEAB000028000001003835000001
Source title
SEG Technical Program Expanded Abstracts
Collation
5 p.
Pages
3835-3839
Files
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