Casing collapse risk assessment and depth prediction with a neural network system approach (Record no. 171133)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01950nab a2200205 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2009 xxu |
| 245 00 - TITULO | |
| Título | Casing collapse risk assessment and depth prediction with a neural network system approach |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | nov. 2009 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 16/06/2010 ; 16/06/2010 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 7 p. ; 156-162 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor. A large carbonate oil field in Iran is suffering from severe casing collapses. 48 casing collapses have been occurred due to reservoir compaction, poro-elastic effects and corrosion. The application of neural networks for predicting casing collapses using complex multi-dimensional field data has been undertaken. This paper shows how a neural network (ANN) system can be trained based on the parameters affecting casing collapse to estimate the potential of collapse of wells to be drilled as well as the current wells producing in the field. The potential use of this type of analysis is large in that it can be linked as a critical risking parameter in future field development analysis. Being able to quantify the potential for collapse of a well in the future can give management the foundation for a better financial decision making on what wells and where to drill them with the potential for the larger net return on the investment. The estimated collapse and corresponding depth could also benefit in the type of casing design and completion method to be selected as well as workover designs. Interpretation of the neural network results, together with engineering judgment, allowed us to conclude that using this method is technically feasible for predicting casing collapses in this field. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 1-2 |
| 773 0# - CORRECCIÓN | |
| Título | Journal of Petroleum Science & Engineering |
| Partes relacionadas | 69 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Salehi, Saeed |
| 9 (RLIN) | 42893 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Hareland, Geir |
| 9 (RLIN) | 10286 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Ganji, Mehdi |
| 9 (RLIN) | 42894 |
| 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 | 200045991 | 200045991 | 05/03/2026 | Artículo de Revista |
