SPE Journal
Volume 15,
Number 2,
June 2010,
pp. 509-525
Summary
With the ensemble Kalman filter (EnKF) or smoother (EnKS), it is easy to
adjust a wide variety of model parameters by assimilation of dynamic data. We
focus first on the case where realizations and estimates of the depths of the
initial fluid contacts, as well as gridblock rock-property fields, are
generated by matching production data with the EnKS. Then we add the parameters
defining power law relative permeability curves to the set of parameters
estimated by assimilating production data with EnKS. The efficiency of EnKF and
EnKS arises because data are assimilated sequentially in time and so "history
matching data" requires only one forward run of the reservoir simulator for
each ensemble member. For EnKS and EnKF to yield reliable characterizations of
the uncertainty in model parameters and future performance predictions, the
updated reservoir-simulation variables (e.g., saturations and pressures) must
be statistically consistent with the realizations of these variables that would
be obtained by rerunning the simulator from time zero using the updated model
parameters. This statistical consistency can be established only under
assumptions of Gaussianity and linearity that do not normally hold. Here, we
use iterative EnKS methods that are statistically consistent, and show that,
for the problems considered here, iteration significantly improves the
performance of EnKS.
© 2009. Society of Petroleum Engineers
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History
- Original manuscript received:
28 October 2008
- Meeting paper published:
2 February 2009
- Revised manuscript received:
13 March 2009
- Manuscript approved:
19 March 2009
- Published online:
17 December 2009
- Version of record:
17 June 2010
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