Even though the ensemble Kalman filter (EnKF) is widely used, history
matching reservoirs with facies description has proven to be a major challenge.
A preferred technique for estimating large-scale facies fields within the
petroleum industry is still missing. In this paper, we present a new approach
to this problem. Instead of applying the EnKF directly to facies realizations,
the approach applies a transformation of facies fields to a specific level-set
function, representing distances between facies types. This ensures better
agreement with the EnKF Gaussianity assumptions, and the method always returns
facies realizations with geological authenticity. The method also offers large
flexibility in generating the initial ensemble, which can be performed using
any geostatistical tool. Furthermore, no modifications of the standard EnKF
equations are needed.
The methodology is evaluated on two synthetic examples with increasing
complexity. In both examples, we consider reservoirs with channel structure.
The results presented show that the updated models give large improvements in
matching the measurements, and the uncertainty of the models is decreased.
Further, recovery of the true petrophysical parameters is highly dependent on
sufficient information in the measurements, but in one of the examples
considered we are able to completely recover the true channel structure.
Additional improvements in the quality of the updated facies fields are
obtained by proper handling of the distances close to the reservoir boundaries,
and by conditioning on specific statistical measures to better preserve prior
information about channel properties.
© 2011. Society of Petroleum Engineers
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- Original manuscript received:
3 October 2009
- Meeting paper published:
8 December 2009
- Revised manuscript received:
7 September 2010
- Manuscript approved:
6 October 2010
- Published online:
3 August 2011
- Version of record:
13 March 2012