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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Ali, M. A. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Abillo, Andre
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2021Review of Current Gaps in Microbiologically Influenced Corrosion (MIC) Failure Investigations in Alberta’s Oil and Gas Sector
- 2021Failure Investigation of Microbiologically Influenced Corrosion in Alberta’s Oil and Gas Upstream Pipeline Operations – Trends and Gaps
- 2021Microbiological Tests Used to Diagnose Microbiologically Influenced Corrosion (MIC) in Failure Investigations
- 2020Integration of State-of-the-Art Methods for Assessing Possible Failures due to Microbiologically Influenced Corrosion
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document
Failure Investigation of Microbiologically Influenced Corrosion in Alberta’s Oil and Gas Upstream Pipeline Operations – Trends and Gaps
Abstract
A conclusive microbiologically influenced corrosion (MIC) investigation requires the integration of multiple layers of evidence related to (1) microbiological, (2) chemical, (3) metallurgical, and (4) operating parameters. The integration between these four groups of information interplay differently depending on the overall combination of data. In order to evaluate current trends and gaps in MIC failure investigations, pipeline incidents in the upstream oil and gas sector of Alberta, Canada were reviewed. It was observed that MIC accounted for 13.6% of the of the pipeline internal corrosion incidents that happened between 2017 and 2019.It was also found that microbiological evidence was not considered in 70% of the cases, in contrast to the higher prevalence (≥ 90%) of abiotic factors (chemical, metallurgical, operating) taken into consideration. This presentation will list the most common trends and gaps found in these failure assessments while it also sheds light on the pieces of data required to conclusively diagnose MIC.