A predictor-corrector algorithm to estimate the fractional flow in oil-water models
We introduce a predictor-corrector algorithm to estimate parameters in a nonlinear hyperbolic problem. It can be used to estimate the oil-fractional flow function from the Buckley-Leverett equation. The forward model is non-linear: the sought- for parameter is a function of the solution of the equat...
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| Autores principales: | , |
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| Formato: | Artículo publishedVersion |
| Lenguaje: | Inglés |
| Publicado: |
2008
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_17426588_v135_n_p_Savioli |
| Aporte de: |
| Sumario: | We introduce a predictor-corrector algorithm to estimate parameters in a nonlinear hyperbolic problem. It can be used to estimate the oil-fractional flow function from the Buckley-Leverett equation. The forward model is non-linear: the sought- for parameter is a function of the solution of the equation. Traditionally, the estimation of functions requires the selection of a fitting parametric model. The algorithm that we develop does not require a predetermined parameter model. Therefore, the estimation problem is carried out over a set of parameters which are functions. The algorithm is based on the linearization of the parameter-to-output mapping. This technique is new in the field of nonlinear estimation. It has the advantage of laying aside parametric models. The algorithm is iterative and is of predictor-corrector type. We present theoretical results on the inverse problem. We use synthetic data to test the new algorithm. © 2008 IOP Publishing Ltd. |
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