Review and Gap Analysis of MIC Failure Investigation Methods in Alberta's Oil and Gas Sector
Bibliographic record
Abstract
This chapter highlights a review and analysis of MIC-related pipeline incidents in the province of Alberta, Canada, over a three-year period (2017–2019). The intent of this work was to quantify the occurrence of MIC failures relative to other corrosion mechanisms, and to conduct a gap analysis of MIC failure investigation techniques being used relative to the current state of the art. Over this three-year period, MIC was found to be responsible for 13.6% and 4.8% of all pipeline leak incidents due to internal and external corrosion, respectively, either as the main failure mechanism or as a contributing factor. Most of these failures were seen to occur in small diameter upstream pipelines (with less than or equal to 220.3 mm outside diameter) carrying mainly multiphase fluids (oil-water emulsions) or produced water. In terms of the failure investigation methods currently being used, it was noted that there was some inconsistency among reports and a number of important gaps were identified. Various assessments lacked microbiological test data, in particular, tests which specifically identify microbial functional groups or speciation, which is critical to confirm observed corrosion mechanisms. Furthermore, a number of these assessments identified MIC primarily on the basis of corrosion morphology, which has been shown to be an incorrect assumption and approach without additional evidence. Details related to sampling methods were also lacking in these assessments, which created some uncertainty as to the quality of data obtained. Overall, most assessments did a reasonable job in characterizing and including chemical (solids, fluids and corrosion products), metallurgical, and operating data. However, the integration of these various layers of evidence (i.e., connecting corrosion to microbiological activity, and eliminating possible abiotic corrosion mechanisms) was missing in many reports and would have significantly strengthened the final assessment conclusions. It is hoped that this analysis 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 of MIC failures in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".