Adaptive multi-scale ensemble-based history matching of highly detailed models.

@inproceedings{GentilhommeGM2013,
 abstract = { We present a new history matching approach designed for highly detailed seismic derived prior models. An ensemble-based optimization method is used to integrate production data and quantify associated uncertainties. An adaptive multi-scale wavelet parameterization is applied to stabilize the inversion and preserve the compatibility of history matched models with seismic data by first modifying a few low frequency parameters and then progressively allowing more important updates on a limited number of sensitive parameters of higher resolution. We show numerically that this adaptive multi-scale method avoids unnecessary updates and reduces noise, which are typically observed in standard ensemble-based methods when using a small ensemble. The new method is validated using a synthetic example. We observe that the final realizations better preserve the spatial distribution of the prior models, are less noisy and also fit the production data better than the realizations updated using a standard gridblock method. },
 author = { Gentilhomme, Théophile AND Oliver, Dean S. AND Mannseth, Trond AND Moyen, Rémi AND Caumon, Guillaume AND Doyen, Philippe },
 booktitle = { Proc. 33rd Gocad Meeting, Nancy },
 title = { Adaptive multi-scale ensemble-based history matching of highly detailed models. },
 year = { 2013 }
}