Assessing Microbial Corrosion Risk on Offshore Crude Oil Production Topsides under Conditions of Nitrate and Nitrite Treatment for Souring
Bibliographic record
Abstract
Oilfield souring is a detrimental effect caused by sulfate-reducing microorganisms that reduce sulfate to sulfide during their respiration process. Nitrate or nitrite can be used to mitigate souring, but may also impart a corrosion risk. Produced fluids sampled from the topside infrastructure of two floating, production, storage, and offloading (FPSO) vessels (Platform A and Platform B) were assessed for microbial corrosion under nitrate and nitrite breakthrough conditions using microcosm tests incubated at 54 °C. Microbial community compositions on each individual FPSO were similar, while those between the two FPSO vessels differed. Platform B microbial communities responded as expected to nitrate breakthrough conditions, where nitrate-reducing activity was enhanced and sulfate reduction was inhibited. In contrast, nitrate treatments of Platform A microbial communities were not as effective in preventing sulfide production. Nitrite breakthrough conditions had the strongest sulfate reduction inhibition in samples from both platforms, but exhibited the highest pitting density. Live experimental replicates with no nitrate or nitrite additive yielded the highest general corrosion rates in the study (up to 0.48 mm/year), while nitrate- or nitrite-treated fluids revealed general corrosion rates that are considered low or moderate (<0.12 mm/year). Overall, the results of this study provide a description of nitrogen- and sulfur-based microbial activities under thermophilic conditions, and their risk for MIC that can occur along fluid processing lines on FPSO topsides that process fluids during offshore oil production operations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".