Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs (Record no. 187032)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01737nab a2200193 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2011 xxu |
| 245 00 - TITULO | |
| Título | Permeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | agos. 2011 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 11/04/2012 ; 11/04/2012 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 8 p. ; 497 - 504 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor Permeability is the most important parameter for precise reservoir description and modeling. Despite the advances and modification in different methods for permeability evaluation such as well testing and well logging, the most exact method is core analysis, which is expensive and time consuming. Because of the well logging data availability in most drilled wells, attempts have been made to utilize artificial neural networks for identification of the relationship, which may exist between the logging data and core permeability. In this study, a new approach based on hybrid neural genetic algorithm has been designed to predict permeability from the well logging data in one of the Iranian heterogeneous oil reservoirs. This approach is based on reservoir zonation according to geology characteristics and sorting the data in the same manner. The predicted permeability was compared to core permeability and it shows that permeability prediction based on designing separate networks for each zone is more accurately than designing single network for all of zones. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 2 |
| 773 0# - CORRECCIÓN | |
| Título | Journal of Petroleum Science & Engineering |
| Partes relacionadas | 78 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Kaidani, Hossein |
| 9 (RLIN) | 52389 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Mohebbi, Ali |
| 9 (RLIN) | 52390 |
| 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 | 200059137 | 200059137 | 06/03/2026 | Artículo de Revista |
