Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo
Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate...
में बचाया:
| मुख्य लेखक: | |
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| अन्य लेखक: | |
| स्वरूप: | Dissertação |
| भाषा: | por |
| प्रकाशित: |
Universidade Federal do Rio Grande do Norte
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| विषय: | |
| ऑनलाइन पहुंच: | https://repositorio.ufrn.br/jspui/handle/123456789/12970 |
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| सारांश: | Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds
vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large
noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform.
Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite
satisfactory for offshore wells and matched real requisites for utilization |
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