Predicting CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation (ACE) Algorithm (Record no. 189436)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02513nab a2200229 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2015 xxu ing |
| 041 ## - IDIOMA | |
| Idioma | Inglés |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Alomair, O. |
| 9 (RLIN) | 55121 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Malallah, A. |
| 9 (RLIN) | 55122 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Elsharkawy |
| 9 (RLIN) | 55123 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Iqbal, M. |
| 9 (RLIN) | 55124 |
| 245 00 - TITULO | |
| Título | Predicting CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation (ACE) Algorithm |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Fecha de publicación, distribución, etc. | nov./dic. 2015 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 28/01/2016 ; 28/01/2016 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 15 p. ; 967-982 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor: Miscible gas injection is one of the most important enhanced oil recovery (EOR) approaches for increasing oil recovery. Due to the massive cost associated with this approach a high degree of accuracy is required for predicting the outcome of the process. Such accuracy includes, the preliminary screening parameters for gas miscible displacement; the "Minimum Miscibility Pressure" (MMP) and the availability of the gas. All conventional and stat-of-art MMP measurement methods are either time consuming or decidedly cost demanding processes. Therefore, in order to address the immediate industry demands a nonparametric approach, Alternating Conditional Expectation (ACE), is used in this study to estimate MMP. This algorithm Breiman and Friedman [Brieman L., Friedman J.H. (1985) J. Am. Stat. Assoc. 80, 391, 580-619]estimates the transformations of a set of predictors (here C1, C2, C3, C4, C5, C6, C7+, CO2, H2S, N2, Mw5+, Mw7+ and T) and a response (here MMP) that produce the maximum linear effect between these transformed variables. One hundred thirteen MMP data points are considered both from the relevant published literature and the experimental work. Five MMP measurements for Kuwaiti Oil are included as part of the testing data. The proposed model is validated using detailed statistical analysis; a reasonably good value of correlation coefficient 0.956 is obtained as compare to the existing correlations. Similarly, standard deviation and average absolute error values are at the lowest as 139 psia (8.55 bar) and 4.68% respectively. Hence, it reveals that the results are more reliable than the existing correlations for pure CO2 injection to enhance oil recovery. In addition to its accuracy, the ACE approach is more powerful, quick and can handle a huge data. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 6 |
| 773 0# - CORRECCIÓN | |
| Título | Oil and Gas Science and Technology |
| Partes relacionadas | 70 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| Fuente de clasificación | Dewey Decimal Classification |
| 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 | 200061672 | 200061672 | 06/03/2026 | Artículo de Revista |
