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- W4300529947 abstract "In the strong-constraint formulation of the history-matching problem, we assume that all the model errors relate to a selection of uncertain model input parameters. One does not account for additional model errors that could result from, e.g., excluded uncertain parameters, neglected physics in the model formulation, the use of an approximate model forcing, or discretization errors resulting from numerical approximations. If parameters with significant uncertainties are unaccounted for, there is a risk for an unphysical update, of some uncertain parameters, that compensates for errors in the omitted parameters. This paper gives the theoretical foundation for introducing model errors in ensemble methods for history matching. In particular, we explain procedures for practically including model errors in iterative ensemble smoothers like ESMDA and IES. Also, we demonstrate the impact of adding (or neglecting) model errors in the parameter-estimation problem." @default.
- W4300529947 created "2022-10-03" @default.
- W4300529947 creator A5019826201 @default.
- W4300529947 date "2018-06-01" @default.
- W4300529947 modified "2023-10-14" @default.
- W4300529947 title "Accounting for model errors in iterative ensemble smoothers" @default.
- W4300529947 doi "https://doi.org/10.48550/arxiv.1806.00237" @default.
- W4300529947 hasPublicationYear "2018" @default.
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