000			02717nab a2200205 4500
005			20260520001751.0
008			260224s2009 xxu
245	0	0	_aData assimilation for nonlinear problems by ensemble Kaman filter with reparameterization
260			_a _b _cmayo 2009
270			_a07/06/2010 ; 07/06/2010
300			_a14 p. ; 1-14
520			_aTranscripción del resumen del autor. Owing to its simplicity and efficiency, the ensemble Kalman filter (EnKF) is being used to assimilate static and dynamic measurements to continuously update reservoir properties and responses. Many EnKF implementations have shown promising results even when applied to multiphase flow history matching problems. A Gaussian density for model parameters and state variables is an implicit requirement for obtaining satisfactory estimates through the EnKF or its variants. The EnKF may not work properly when the relationship between model parameters, state variables, and observations are strongly nonlinear and the resulting joint probability distribution is non-Gaussian. For instance, near the displacement front of an immiscible flow, use of the EnKF to directly update saturation may lead to non-physical results. In this work, we address the non-Gaussian effect through a change in parameterization. Instead of directly updating the saturation, the time of saturation arrival (at a particular saturation) is included in the state vector. The time variable is correlated with the reservoir properties and other reservoir responses and its density is better approximated by a Gaussian distribution. After updating the time of saturation arrival through the EnKF, the updated arrival time distribution is transformed back to estimate the saturation of the eservoir. The new approach has better performance in the presence of strong non-Gaussianities but requires a larger computation time than does the traditional EnKF, which works well when the Gaussian assumption is not trongly violated. In order to achieve both accuracy and efficiency, the EnKF with reparameterization can be used in conjunction with the traditional EnKF as an option to account for possible highly non-Gaussian densities. The EnKF with reparameterization is illustrated with a problem under highly non-Gaussian conditions, and the effectiveness of the combination of the new approach and the traditional EnKF is demonstrated with history matching of multiphase flow in a heterogeneous reservoir.
581			_a1-2
773	0		_tJournal of Petroleum Science & Engineering _g66
942			_cARTICULO
100	1		_aChen, Yan _942191
100	1		_aOliver, Dean S. _942192
100	1		_aZhang, Dongxiao _942193
999			_c170749 _d170749