Development and testing of proxy models for screening cyclic pressure pulsing process in a depleted, naturally fractured reservoir (Record no. 176997)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02182nab a2200205 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2010 xxu |
| 245 00 - TITULO | |
| Título | Development and testing of proxy models for screening cyclic pressure pulsing process in a depleted, naturally fractured reservoir |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | ago. 2010 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 20/09/2010 ; 20/09/2010 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 13 p. ; 73-85 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor. Cyclic pressure pulsing using CO2 and N2 is an effective improved oil recovery method in naturally fractured reservoirs. Determining the optimum design parameters for the process is an arduous task due to the computational cost of simulating a large number of injection schemes. In this paper, we present neural-network based proxy models that mimic a reservoir simulation model and provide estimated quantities of critical performance indicators. The proxy models are trained with a set of representative design scenarios. These design scenarios are run in a compositional, dual-porosity reservoir model and corresponding performance indicators are collected. Cyclic pressure pulsing process is modeled using two huff ‘n’ puff design schemes with variable and constant cyclic injection volumes. The reservoir model is constructed based on reservoir characteristics of the Big Andy Field in Kentucky which is a depleted, naturally fractured reservoir with stripper-well production. Predictive capability and accuracy of developed proxy models are checked by comparing simulation outputs with proxy outputs. It is observed that neural-network based proxy models are able to accurately predict the performance indicators including the peak rate, time to reach the peak rate, cycle flow rates, incremental oil production, and gas–oil ratio. The proposed methodology is practical and computationally efficient in structuring more effective decisions towards the optimum design of the process. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 1-2 |
| 773 0# - CORRECCIÓN | |
| Título | Journal of Petroleum Science & Engineering |
| Partes relacionadas | 73 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Artun, E. |
| 9 (RLIN) | 46878 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Ertekin, T. |
| 9 (RLIN) | 5736 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Watson, R. |
| 9 (RLIN) | 46879 |
| 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 | 200048932 | 200048932 | 05/03/2026 | Artículo de Revista |
