Use of Molecular Methods (Pyrosequencing) for Evaluating MIC Potential in Water Systems for Oil Production in the North Sea
Bibliographic record
Abstract
Abstract The application of molecular methods, like pyrosequencing of 16S rRNA genes, has now accelerated to the point where identification of microbial communities involved in microbiologically influenced corrosion (MIC) may become routine. We have used this technology to characterize samples obtained from North Sea oil-producing platforms. Pigging solids were collected from a pipeline that carried a high water content, had a low velocity and cooled rapidly (mesophilic growth conditions), causing high corrosion rates to be expected. A second set of samples was obtained from a platform subjected to produced water reinjection (PWRI). The samples included produced water and PWRI water, a mixture of produced water and seawater. Historically, both locations showed high sulfate-reducing bacteria (SRB) numbers and the system was deemed to be under corrosion threat. Sulfate concentrations were 2600 ppm in the PWRI water and 170 ppm in produced water, suggesting that sulfate reduction occurred. The pigging solids were found to have high counts of SRB, identified by pyrosequencing to be mostly Desulfovibrio. However, high fractions (13–50%) of sulfur-reducing bacteria (SuRB), of the order Desulfuromonadales were also found. Pyrosequencing indicated that produced water also had more SuRB (48%) than SRB (18%). The PWRI water had a high fraction of the sulfide-oxidizing bacterium (SOB) Arcobacter (25%). The data suggest that mixing of produced water and oxygenated seawater gives rise to chemical and microbial (Arcobacter) oxidation of sulfide to sulfur, which is then reduced to sulfide by the SuRB using oil organics as electron donor. The predominance of SOB and SuRB indicates that sulfur is prevalent in the system, which indicates that MIC may be involved in this environment. Application of molecular methods greatly facilitates the collection of this information and is thus an important tool in assessing MIC threat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".