Interpreting Pressure and Flow-Rate Data From Permanent Downhole Gauges by Use of Data-Mining Approaches (Record no. 187595)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02796nab a2200205 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2013 xxu ing |
| 041 ## - IDIOMA | |
| Idioma | Inglés |
| 245 00 - TITULO | |
| Título | Interpreting Pressure and Flow-Rate Data From Permanent Downhole Gauges by Use of Data-Mining Approaches |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | feb. 2013 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 15/04/2013 ; 15/04/2013 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 13 p. ; 69-82 |
| 520 ## - RESUMEN, ETC | |
| Resumen | Transcripción del resumen del autor: The permanent downhole gauge (PDG) is a promising tool for reservoir testing but has yet to reach its full potential. Generally, conventional well-testing methods are most able to use small sections of constant-flow-rate data. However, data mining, a newly developed technique in computer science, is a tool that can reveal the relationship among variables from large volumes of data. The application of data-mining algorithms to synthetic and field data has been successful in extracting the reservoir model from variable-flow-rate and pressure-transient data. In fact, because of uncertainty in flow-rate measurements, this technique is one of the few ways to make use of the flow-rate data that are now available with some modern PDG tools. The application is conducted in two steps. First, the pressure and flow-rate data from the PDG are used to train a nonparametric data-mining algorithm. The reservoir model is obtained implicitly in the form of polynomials in a high-dimensional Hilbert space defined by kernel functions when the algorithm converges after being trained to the data. Next, a specific flow-rate input (for example, constant rate) is fed into the data-mining algorithm. The datamining algorithm will make a pressure prediction subject to the input flow rate. Because the data-mining algorithm has already obtained the reservoir model, the pressure prediction is expected to be the reservoir response given the constant flow rate. Therefore, the proposed constant flow rate and the predicted pressure reveal the reservoir model underlying the variable PDG data, without needing to specify in advance which reservoir model is to be used. Three methods (Methods A, B, and C), differing by input vectors, kernel functions, and presence of breakpoints detection, are proposed in the paper. Synthetic noisy data and real field data were used to test this approach. Methods B and C were able to satisfactorily recover the wellbore/reservoir model in most considered cases. Even in extreme cases when the flow-rate data are noisy and changing frequently, and in the absence of any shut-ins, the method was still able to extract the reservoir models. |
| 581 ## - ESTADO DE COLECCIÓN | |
| Estado de colección | 1 |
| 773 0# - CORRECCIÓN | |
| Título | SPE Journal |
| Partes relacionadas | 18 |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Artículo de Revista |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Liu, Yang |
| 9 (RLIN) | 52987 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Horne, Roland N. |
| 9 (RLIN) | 17046 |
| 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 | 200059728 | 200059728 | 06/03/2026 | Artículo de Revista |
