Identification and Analysis of Biocides Effective Against Sessile Organisms
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Microbiologically influenced corrosion (MIC) is a major problem in the oil and gas industry. Although the exact mechanism by which this occurs is not well understood, it is recognized that byproducts produced by sessile bacteria located on the metal surface are responsible for the corrosion. However, many of the biocide treatment programs that have been developed thus far have focused only on planktonic organisms, ignoring the root cause of the problem. The goal of this research was to develop and implement sessile monitoring and analysis capabilities to assist in biocide selection. Initial laboratory testing was performed using a representative selection of bacteria inoculated into a closed flow loop system containing removable biostuds. Sessile bacteria populations were analyzed before and after biocide treatment using serial dilutions and denaturing gradient gel electrophoresis (DGGE). A biocide/surfactant combination shown to be effective in the lab was tested in a field trial to demonstrate a correlation between laboratory testing and field use. Data collected in the field was analyzed by quantitative PCR (qPCR) as well as DGGE. The biocide/surfactant tested in the laboratory led to a 3-log reduction in sessile bacteria without regrowth 24 hours after treatment. Bacterial enumeration determined by serial dilution was confirmed by DGGE analysis. This biocide/surfactant combination was also tested in a field trial where a 3- to 5-log reduction in bacterial numbers was determined by qPCR, a dramatic reduction in bacterial species observed by DGGE, and reduced pitting of the corrosion coupons identified. In conclusion, we have implemented new testing capabilities that allow us to identify biocides effective at removing sessile organisms in the laboratory. Importantly, we have also shown that these laboratory results are recapitulated in field trials. These methods can now be utilized to ensure that the most efficacious biocide is chosen to mitigate bacterial populations that could potentially cause MIC in an asset-specific manner.
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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 | 0.000 | 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) | 0.001 | 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 it