000			02065nab a2200229 4500
005			20260520004412.0
008			260224s2012 xxu ing
041			_aInglés
245	0	0	_aImproved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques
260			_a _b _cmar. 2012
270			_a05/12/2012 ; 05/12/2012
300			_a10 p. ; 152-162
520			_aTranscripción del resumen del autor The ensemble Kalman filter (EnKF) is a sequential Monte Carlo method for solving nonlinear spatiotemporal inverse problems, such as petroleum-reservoir evaluation, in high dimensions. Although the EnKF has seen successful applications in numerous areas, the classical EnKF algorithm can severely underestimate the prediction uncertainty. This can lead to biased production forecasts and an ensemble collapsing into a single realization. In this paper, we combine a previously suggested EnKF scheme based on dimension reduction in the data space, with an automatic cross-validation (CV) scheme to select the subspace dimension. The properties of both the dimension reduction and the CV scheme are well known in the statistical literature. In an EnKF setting, the former can reduce the effects caused by collinear ensemble members, while the latter can guard against model overfitting by evaluating the predictive capabilities of the EnKF scheme. The model-selection criterion traditionally used for determining the subspace dimension, on the other hand, does not take the predictive power of the EnKF scheme into account, and can potentially lead to severe problems of model overfitting. A reservoir case study is used to demonstrate that the CV scheme can substantially improve the reservoir predictions with associated uncertainty estimates.
581			_a1
773	0		_tSPE Journal _g17
942			_cARTICULO
100	1		_aSaetron, Jon _952825
100	1		_aHove, Joakim _952826
100	1		_aSkjervheim, Jan-Arild _952827
100	1		_avabo, Jon Gustav _952828
999			_c187481 _d187481