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Abstract

Summary

Estimating geo-modeling control-parameters from historical data by means of the EnKF.

Over the last decade the ensemble Kalman filter (EnKF) and other offspring ensemble based methods (here also referred to as EnKF), have attracted attention as promising methods for solving the reservoir history matching problem. The EnKF has successfully been used to estimate flow properties, such as permeability and porosity, of each grid cell in a history matching loop. For more complex problems, such as facies estimation problems, there are challenges. The direct use of EnKF to estimate e.g. porosity and permeability of a facies field could lead to a good history match, but, it would ruin the geological realism of facies fields, i.e., the boundaries between facies would be smeared out. Several papers have addressed this problem by estimating facies boundaries instead of, or in addition to, the petrophysical properties, in order to maintain the geological realism. The facies boundaries are typically reparameterized using variables that can be characterized by Gaussian to suit the assumption of EnKF.

In this work we attack the problem from a different angle; we perform ‘‘the Big-Loop’’ update, i.e., the geomodel control-parameters are updated using production data and updated facies models are generated with updated control-parameters, using the same geo-modeling work-flow, so that geological realism is naturally preserved. The implementation is referred to as the Big-Loop as we update the geological models not only reservoir models. To perform the investigation we have coupled a geostatistical simulator, a black oil flow simulator and the EnKF. The Big-Loop is tested on examples with facies models generated using the truncated PluriGaussian method. Based on the results found in this work the following can be stated:

* The update models satisfy geological constraints imposed in the geo-modeling work-flow. The match to historical data is improved by updating both local and global geo-parameters,

* It seems beneficial to include both global and local geo-parameters in the Big-Loop approach compared to only local or only global parameters.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.20141812
2014-09-08
2024-04-18

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141812
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Estimating Geo-modeling Control-parameters from Historical Data by Means of the EnKF
Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20141812
/content/papers/10.3997/2214-4609.20141812
/content/papers/10.3997/2214-4609.20141812
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