Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques (Record no. 187481)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02065nab a2200229 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2012 xxu ing |
| 041 ## - IDIOMA | |
| Idioma | Inglés |
| 245 00 - TITULO | |
| Título | Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | mar. 2012 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 05/12/2012 ; 05/12/2012 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 10 p. ; 152-162 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor The ensemble Kalman filter (EnKF) is a sequential Monte Carlo method for solving nonlinear spatiotemporal inverse problems, such as petroleum-reservoir evaluation, in high dimensions. Although the EnKF has seen successful applications in numerous areas, the classical EnKF algorithm can severely underestimate the prediction uncertainty. This can lead to biased production forecasts and an ensemble collapsing into a single realization. In this paper, we combine a previously suggested EnKF scheme based on dimension reduction in the data space, with an automatic cross-validation (CV) scheme to select the subspace dimension. The properties of both the dimension reduction and the CV scheme are well known in the statistical literature. In an EnKF setting, the former can reduce the effects caused by collinear ensemble members, while the latter can guard against model overfitting by evaluating the predictive capabilities of the EnKF scheme. The model-selection criterion traditionally used for determining the subspace dimension, on the other hand, does not take the predictive power of the EnKF scheme into account, and can potentially lead to severe problems of model overfitting. A reservoir case study is used to demonstrate that the CV scheme can substantially improve the reservoir predictions with associated uncertainty estimates. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 1 |
| 773 0# - CORRECCIÓN | |
| Título | SPE Journal |
| Partes relacionadas | 17 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Saetron, Jon |
| 9 (RLIN) | 52825 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Hove, Joakim |
| 9 (RLIN) | 52826 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Skjervheim, Jan-Arild |
| 9 (RLIN) | 52827 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | vabo, Jon Gustav |
| 9 (RLIN) | 52828 |
| Biblioteca propietaria | Biblioteca actual | Fecha de adquisición | Inventario | Total de préstamos | Inventario | Fecha de carga | Tipo de item KOHA |
|---|---|---|---|---|---|---|---|
| Biblioteca Alejandro Angel Bulgheroni | Biblioteca Alejandro Angel Bulgheroni | 06/03/2026 | 200059600 | 200059600 | 06/03/2026 | Artículo de Revista |
