Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

• Main Authors: Geir Evensen, Patrick N. Raanes, Andreas S. Stordal, Joakim Hove
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-10-01
• Series: Frontiers in Applied Mathematics and Statistics
• Subjects: EnRML; iterative ensemble smoother; history matching; data assimilation; inverse methods; parameter estimation
• Online Access: https://www.frontiersin.org/article/10.3389/fams.2019.00047/full

Raanes et al. [1] revised the iterative ensemble smoother of Chen and Oliver [2, 3], denoted Ensemble Randomized Maximum Likelihood (EnRML), using the property that the EnRML solution is contained in the ensemble subspace. They analyzed EnRML and demonstrated how to implement the method without the use of expensive pseudo inversions of the low-rank state covariance matrix or the ensemble-anomaly matrix. The new algorithm produces the same result, realization by realization, as the original EnRML method. However, the new formulation is simpler to implement, numerically stable, and computationally more efficient. The purpose of this document is to present a simple derivation of the new algorithm and demonstrate its practical implementation and use for reservoir history matching. An additional focus is to customize the algorithm to be suitable for big-data assimilation of measurements with correlated errors. The computational cost of the resulting “ensemble sub-space” algorithm is linear in both the dimension of the state space and the number of measurements, also when the measurements have correlated errors. The final algorithm is implemented in the Ensemble Reservoir Tool (ERT) for running and conditioning ensembles of reservoir models. Several verification experiments are presented.