Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems (Record no. 175680)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02288nab a2200205 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2010 xxu |
| 245 00 - TITULO | |
| Título | Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems |
| Subtítulo | A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | mayo 2010 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 03/08/2010 ; 03/08/2010 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 11 p. ; 175-185 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor. Nuclear Magnetic Resonance (NMR) log provides useful information for petrophysical study of the hydrocarbon bearing intervals. Free fluid porosity (effective porosity), rock permeability and bound fluid volume (BFV) could be obtained by processing and interpretation of NMR data. The present study proposes an improved strategy to make a quantitative correlation between the NMR log parameters and conventional well logs by integration of different intelligent systems using the concept of committee machine. The proposed committee machine with intelligent systems (CMIS) combines the results of Fuzzy Logic (FL), Neuro-Fuzzy (NF) and Neural Network (NN) algorithms for overall estimation of the NMR log parameters from conventional well log data. It assigns a weight factor to each of the individual intelligent algorithms showing its contribution in overall prediction. The weight factors are derived in two ways: simple averaging and weighted averaging. In the weighted averaging method a genetic algorithm (GA) was employed to obtain the optimal contribution of each algorithm in construction of the CMIS. The proposed methodology was applied to the South Pars gas field, Persian Gulf Basin. The petrophysical logs from two wells were used for constructing the intelligent models and a third well from the field was used to evaluate the reliability of the developed models. The results indicate the higher performance of the GA optimized model over the individual intelligent systems performing alone. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 1-2 |
| 773 0# - CORRECCIÓN | |
| Título | Journal of Petroleum Science & Engineering |
| Partes relacionadas | 72 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Mahdi Labani, Mohammad |
| 9 (RLIN) | 46166 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Kadkhodaie-Ilkhchi, Ali |
| 9 (RLIN) | 40764 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Salahshoor, Karim |
| 9 (RLIN) | 46167 |
| 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 | 200047578 | 200047578 | 05/03/2026 | Artículo de Revista |
