Sulfur-Reducing Bacteria May Lower Corrosion Risk in Oil Fields by Coupling Oxidation of Oil Organics to Reduction of Sulfur-Polysulfide to Sulfide
Bibliographic record
Abstract
Abstract Sulfate-reducing bacteria are generally considered to have a profound impact on the petroleum industry as their sulfide production activity contributes to reservoir souring and pipeline corrosion. SRB can be controlled by injecting biocides into pipelines and above-ground facilities. A recent “green” alternative for controlling reservoir souring is to inject nitrate, as nitrate is relatively harmless and is ultimately reduced to nitrogen gas. Resident nitrate-reducing bacteria (NRB) reduce nitrate to nitrite, which is a strong inhibitor of SRB, thereby inhibiting sulfide production. However, NRB-mediated oxidation of sulfide with nitrate and/or chemical reaction between nitrite and sulfide can generate sulfur-polysulfide (S-PS), which can expedite corrosion. S-PS is also rapidly formed by chemical reactions, when sour produced waters, containing substantial sulfide concentrations, are exposed to air. Once formed, S-PS can be removed by either of two alternative routes, which may thus reduce corrosion risk. In the presence of (i) excess electron acceptor (e.g. nitrate), NRB may further oxidize the S-PS to sulfate, whereas in the presence of (ii) excess electron donor (oil organics, e.g. acetate) the S-PS may be reduced back to sulfide. A specialized group of sulfur-reducing bacteria catalyzes this reaction. A representative of this group, Desulfuromonas acetoxidans, derives energy for growth from the reaction: 4 sulfur + acetate = 4 sulfide + 2 CO2. Because oil field waters tend to be electron donor rich and electron acceptor poor, one would expect S-PS to be removed by the second route. A survey of the microbial community in produced waters from an oil field with low bottomhole temperatures indicated Desulfuromonas species to be common. Hence, once S-PS is formed by reaction of excess sulfide with a limiting concentration of nitrate or oxygen, it may subsequently be effectively removed through the activity of sulfur-reducing bacteria such as Desulfuromonas.
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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 | 0.000 |
| 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) | 0.002 | 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".