JOURNAL ARTICLE

Microbially induced corrosion issues in the underground buried crude oil and natural gas bearing pipelines: A review.

  • Published In: Materials & Corrosion / Werkstoffe und Korrosion, 2024, v. 75, n. 2. P. 197 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bairi, Lipika Rani; Bhuyan, Pallabi; Ghosh, Anirban; Narang, Mandeep; Mandal, Sumantra 3 of 3

Abstract

Microbially induced corrosion (MIC) damage in crude oil and natural gas‐bearing pipelines is a severe threat for the petroleum industries. This is primarily due to the presence of hydrocarbons, which serve as nutrients for microorganisms. The comfortable growth of these microorganisms in hydrocarbon systems ultimately results in enhanced degradation of the pipelines. Hence, to understand the effect of microorganisms on the degradation of petroleum hydrocarbons and its subsequent influence on the corrosion of pipeline materials, a detailed review of the literature has been carried out. This review is primarily focused on the principal factors and the major microorganisms responsible for biofilm development. Furthermore, the role of these microorganisms in causing MIC in underground buried petroleum‐bearing pipelines has been reviewed in detail. Most importantly, the techniques required for studying MIC have been thoroughly discussed. In addition, a few microbial cases associated with various petroleum industries have been reviewed to acquire a clear understanding about the real situation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Materials & Corrosion / Werkstoffe und Korrosion. 2024/02, Vol. 75, Issue 2, p197
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:0947-5117
  • DOI:10.1002/maco.202313950
  • Accession Number:175196323
  • Copyright Statement:Copyright of Materials & Corrosion / Werkstoffe und Korrosion is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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