Outubro 2023 vol. 10 num. 5 - IX Simpósio Internacional de Inovação e Tecnologia

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SELECTING THE DETECTION METHOD FOR A REMOTE LEAKS DETECTION MONITORING SYSTEM AT ONSHORE OIL PRODUCTION FIELDS

SELECTING THE DETECTION METHOD FOR A REMOTE LEAKS DETECTION MONITORING SYSTEM AT ONSHORE OIL PRODUCTION FIELDS

Maia, Danielle Mascarenhas ; Melo, Thamiles Rodrigues de ; Lepikson, Herman Augusto ; Mirre, Reinaldo Coelho ;

Full article:

Pipelines from onshore production fields have characteristics that accentuate leak detection problems. New initiatives on fast and accurate leak detection are critical for this type of scenario. In the literature there is a wide variety of leak detection methods developed and tested. With so much diversity of methods, the process of choosing a detection method for new proposed monitoring systems can become a challenging activity. Therefore, this article proposes a methodology based on six steps for defining the most appropriate detection method.

Full article:

Pipelines from onshore production fields have characteristics that accentuate leak detection problems. New initiatives on fast and accurate leak detection are critical for this type of scenario. In the literature there is a wide variety of leak detection methods developed and tested. With so much diversity of methods, the process of choosing a detection method for new proposed monitoring systems can become a challenging activity. Therefore, this article proposes a methodology based on six steps for defining the most appropriate detection method.

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Palavras-chave: pipelines, leak detection, onshore oil production fields,

Palavras-chave: pipelines, leak detection, onshore oil production fields,

DOI: 10.5151/siintec2023-305738

Referências bibliográficas
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Como citar:

Maia, Danielle Mascarenhas ; Melo, Thamiles Rodrigues de ; Lepikson, Herman Augusto ; Mirre, Reinaldo Coelho ; "SELECTING THE DETECTION METHOD FOR A REMOTE LEAKS DETECTION MONITORING SYSTEM AT ONSHORE OIL PRODUCTION FIELDS", p. 140-146 . In: . São Paulo: Blucher, 2023.
ISSN 2357-7592, DOI 10.5151/siintec2023-305738

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