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Abstract

Summary

There is an increasing interest in Multi-fidelity Modeling within computational statistics research in recent years. Multilevel ensemble-based data assimilation (MLDA), taking advantage of Multi-fidelity modeling, is a novel approach for reservoir history-matching. This method has been proposed to overcome the potential sampling errors that are encountered in conventional ensemble-based data assimilation techniques. Ensemble-based methods have been successful in history-matching of large cases but the limit in computational resources normally results in the ensemble size to be confined to about 100, which can yield to sampling error. In order to address the problem of sampling error, localization has been proposed which handles the problem of non-local spurious correlations but does not allow for true non-local correlations. The basic concept of MLDA revolves about allocation of resources for computation of models on a hierarchy of accuracy and computational cost. Utilization of models with a lower computational cost enables a significant increase in the ensemble size. Doing so, it brings about the opportunity to trade an appropriate amount of computational accuracy for a better statistical accuracy. In this research, the hierarchy of computational cost is established using a variation of spatial resolutions in the simulation models, and a new scheme called Simultaneous Spatial Multilevel Data Assimilation for multilevel data is investigated on a reservoir model. This method is designed to assimilate the inverted seismic data in a multilevel manner. Accordingly, a set of different spatial resolutions of the model is created and an ensemble of models and their corresponding inverted seismic data are considered for any of the resolutions. The simulations are run for all of the levels and an independent update is performed on any of the levels using the Ensemble Smoother (ES). The reduction of computational cost in coarser resolutions entails a multilevel error which can be quantified and accounted for, by a comparison with the simulations in the finest level. Finally, a cumulative statistical analysis over all ensembles is done to assess the performance in data assimilation. Results obtained from two variants of the new scheme are evaluated and compared to ES with localization and standard ensemble size.

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/content/papers/10.3997/2214-4609.202035091
2020-09-14
2025-04-26

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/content/papers/10.3997/2214-4609.202035091
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A Novel Approach to Multilevel Data Assimilation
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035091
/content/papers/10.3997/2214-4609.202035091
/content/papers/10.3997/2214-4609.202035091
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