Use of Homogeneously-Sized Carbon Steel Ball Bearings to Study Microbially-Influenced Corrosion in Oil Field Samples
Bibliographic record
Abstract
Microbially-influenced corrosion (MIC) contributes to the general corrosion rate (CR), which is typically measured with carbon steel coupons. Here we explore the use of carbon steel ball bearings, referred to as beads (55.0 ± 0.3 mg; Ø = 0.238 cm), for determining CRs. CRs for samples from an oil field in Oceania incubated with beads were determined by the weight loss method, using acid treatment to remove corrosion products. The release of ferrous and ferric iron was also measured and CRs based on weight loss and iron determination were in good agreement. Average CRs were 0.022 mm/yr for eight produced waters with high numbers (10(5)/ml) of acid-producing bacteria (APB), but no sulfate-reducing bacteria (SRB). Average CRs were 0.009 mm/yr for five central processing facility (CPF) waters, which had no APB or SRB due to weekly biocide treatment and 0.036 mm/yr for 2 CPF tank bottom sludges, which had high numbers of APB (10(6)/ml) and SRB (10(8)/ml). Hence, corrosion monitoring with carbon steel beads indicated that biocide treatment of CPF waters decreased the CR, except where biocide did not penetrate. The CR for incubations with 20 ml of a produced water decreased from 0.061 to 0.007 mm/yr when increasing the number of beads from 1 to 40. CRs determined with beads were higher than those with coupons, possibly also due to a higher weight of iron per unit volume used in incubations with coupons. Use of 1 ml syringe columns, containing carbon steel beads, and injected with 10 ml/day of SRB-containing medium for 256 days gave a CR of 0.11 mm/yr under flow conditions. The standard deviation of the distribution of residual bead weights, a measure for the unevenness of the corrosion, increased with increasing CR. The most heavily corroded beads showed significant pitting. Hence the use of uniformly sized carbon steel beads offers new opportunities for screening and monitoring of corrosion including determination of the distribution of corrosion rates, which allows estimation of the probability of high rate events that may lead to failure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".