9:00 am - 10:00 am
A graduate exam seminar is a presentation of the student’s final research project for their degree.
This is an ALES MSc Final Exam Seminar by Andre De Araujo Abilio. This seminar is open to the general public to attend.
https://ualberta-ca.zoom.us/j/97269636766?pwd=SS85U2JmRXMvMGlqUm9CYkNiNU1ZUT09
Meeting ID: 972 6963 6766 | Passcode: 898209
Thesis Topic: Failure Assessment of Pipelines Due to Microbiologically Influenced Corrosion
MSc with Drs. John Wolodko and Torben Lund Skovhus.
Seminar Abstract:
Microbiologically influenced corrosion (MIC) is a degradation mechanism found in industrial settings which results in damage and possible failures in steel infrastructure such as pipelines and facilities used in the oil & gas sector. MIC is a difficult degradation mechanism to diagnose in pipeline systems due to the complex interaction between biotic (i.e., microbial) and abiotic (e.g. fluid chemistry, pipe/vessel metallurgy and operating conditions) factors. Even with available data and information, failure investigators often face a number of challenges in diagnosing MIC such as how to properly integrate the available datasets; questions regarding data accuracy (e.g., confidence in the sampling and/or analysis method used); and lack of available information from operators (e.g., missing data). This is further complicated by the fact that the scientific knowledge about MIC and the methods used to characterize both biotic and abiotic samples are continuing to evolve. Based on these issues, a series of studies were carried out to better understand and improve MIC related failure investigation methods. Using incident statistics and detailed failure reports obtained from the Alberta Energy Regulator, the actual prevalence of MIC related pipeline failures in Alberta’s oil & gas sector was quantified. A gap analysis of failure investigation methods was also performed to assess current knowledge and approaches used in these reports relative to best practices identified in the literature. Finally, using this gap analysis and expert elicitation, a novel expert system based on machine learning was developed to assist non-experts in assessing potential MIC related pipeline failures. It is hoped that this work contributes to a better understanding of the prevalence of MIC in the oil and gas sector, and highlights the key areas necessary to improve the diagnosis and mitigation of MIC failures in the future.
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