The paper presents the application of multi-scale Levenberg-Marquardt ensemblerandomized maximum likelihood(MS-LM-EnRML) method to an offshore field in Campos Basis. The objective of this method is to update an ensemble of spatial properties and flow parameters using observed productiondata while preserving the prior information already carried by the prior models. The final ensemble provides an estimate of the uncertainties and can then be used for productionforecast andinfill drilling projects. Instead of working directly with the gridblock values of the reservoir, the MS-LM-EnRML method performs a wavelet transform of the spatial properties. Based on this parameterization, a multi-scale approach is followed: different subsets of wavelet coefficients are successively updated with the data using the Levenberg-Marquardt ensemble-based data assimilationmethod. Thanks to the compression property of the wavelets, the field properties are well characterized with a small number of wavelet coefficients. The algorithm starts by optimizing an initial sub-set of large-scale coefficients. The parameterization is then progressively refined, and a new optimization is run on the new subset of coefficients. Because the data mismatch is highly reduced by the initial optimizations at large scales, the perturbation of the fine scale coefficients is minimized resulting in a better preservation of the prior models and a reduced level of noise in the final models.

For comparisons, a second history matching was conducted using the ensemble smoother with multiple data assimilation (ES-MDA) method without the multi-scale parameterization. The quality of the data match, the preservation of the prior models and the ensemble variance are used to compare the two methods. The results showed that although a better data matcheswereobtained with ES-MDA, MS-LM-EnRML preserved better the prior models, the geological consistency and the variability of the ensemble. These results indicate that the multi-scale approach is an attractive option especially when a large number of data has to be assimilated (e.g., long production history and 4D seismic data) or when geological features and seismic information of the initial models need to be preserved.

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