Automated clustering based scenario reduction to accelerate robust life-cycle optimization
conference paper
Incorporating uncertainty in reservoir life-cycle optimization has been shown to achieve results of significant practical value. We introduce an automated technique for scenario reduction using clustering techniques to accelerate robust life-cycle optimization. The technique determines, based on a statistical metric, a representative subset of model realizations that correlates with the cumulative distribution function (CDF) of a quantity of interest of the full ensemble. More specifically, the proposed approach addresses the automatic determination of the appropriate number of clusters. A database of clustering results is generated by repeating the inexpensive clustering procedure with different number of clusters. This allows for the construction of a "distance" curve, which is then used to determine the appropriate number of clusters. We have applied the workflow to waterflooding optimization in two synthetic cases where geological uncertainty is characterized through an ensemble of equiprobable model realizations. The optimization based on the subset of representative model realizations obtained from the newly introduced workflow lead to almost the same objective function values compared to the optimization of the full ensemble using approximately 70% fewer simulations. Our results indicate that the proposed automated workflow provides a computationally efficient scheme for optimization under uncertainty.
Topics
TNO Identifier
955033
ISBN
9789462822
Publisher
European Association of Geoscientists and Engineers, EAGE
Source title
16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018, 16th European Conference on the Mathematics of Oil Recovery, ECMOR 2018, 3 September 2018 through 6 September 2018
Pages
1-18
Files
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