Development of artificial neural network models for predicting water saturation and fluid distribution (Record no. 171111)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02445nab a2200205 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2009 xxu |
| 245 00 - TITULO | |
| Título | Development of artificial neural network models for predicting water saturation and fluid distribution |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | oct. 2009 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 16/06/2010 ; 16/06/2010 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 12 p. ; 197-208 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor. We have developed artificial neural network (ANN) models to predict water saturation from log data. Two Middle Eastern sandstone reservoirs were investigated. In the first case, an ANN model was tested on the Haradh formation in Oman using wireline logs and core Dean–Stark data. In the second case, the ANN was used to model the saturation–height function in a complex sandstone reservoir. In the first case study, the model is based on a three-layered neural network structure. The model was successfully tested yielding a prediction of water saturation with a root mean square error (RMSE) of around 0.025 (fraction of pore volume P.V.) and a correlation factor of 0.91 to the test data. Furthermore, the ANN model was shown to be superior to conventional statistical methods such as multiple linear regression, which gave a correlation factor of 0.41. In the second case, the model yielded a saturation–height function with an RMSE of 0.079 (fraction P.V.) in saturation when using core porosity and height above free water level. This is a considerable improvement over conventional methods. The error was also greatly reduced when permeability and a lithology indicator were introduced. A minimum error of 0.045 (fraction P.V.) was obtained when using core data such as height, porosity, permeability, lithology and a functional link. We then used gamma ray, neutron, density, resistivity wireline data and the cation exchange capacity as inputs. Our best case which gave an RMSE error of 0.046 (fraction P.V.) was obtained. The ANN was then used to predict the hydrocarbon saturation in the Gharif formation and good results were obtained. The neural network model proved the robustness of saturation prediction in another field for the same formation. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 3-4 |
| 773 0# - CORRECCIÓN | |
| Título | Journal of Petroleum Science & Engineering |
| Partes relacionadas | 68 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Al-Bulushi, Nabil |
| 9 (RLIN) | 42848 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | King, Peter R. |
| 9 (RLIN) | 42849 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Blunt, Martin J. |
| 9 (RLIN) | 12460 |
| 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 | 05/03/2026 | 200045969 | 200045969 | 05/03/2026 | Artículo de Revista |
