Acetate Production from Oil under Sulfate-Reducing Conditions in Bioreactors Injected with Sulfate and Nitrate
Bibliographic record
Abstract
Oil production by water injection can cause souring in which sulfate in the injection water is reduced to sulfide by resident sulfate-reducing bacteria (SRB). Sulfate (2 mM) in medium injected at a rate of 1 pore volume per day into upflow bioreactors containing residual heavy oil from the Medicine Hat Glauconitic C field was nearly completely reduced to sulfide, and this was associated with the generation of 3 to 4 mM acetate. Inclusion of 4 mM nitrate inhibited souring for 60 days, after which complete sulfate reduction and associated acetate production were once again observed. Sulfate reduction was permanently inhibited when 100 mM nitrate was injected by the nitrite formed under these conditions. Pulsed injection of 4 or 100 mM nitrate inhibited sulfate reduction temporarily. Sulfate reduction resumed once nitrate injection was stopped and was associated with the production of acetate in all cases. The stoichiometry of acetate formation (3 to 4 mM formed per 2 mM sulfate reduced) is consistent with a mechanism in which oil alkanes and water are metabolized to acetate and hydrogen by fermentative and syntrophic bacteria (K. Zengler et al., Nature 401:266-269, 1999), with the hydrogen being used by SRB to reduce sulfate to sulfide. In support of this model, microbial community analyses by pyrosequencing indicated SRB of the genus Desulfovibrio, which use hydrogen but not acetate as an electron donor for sulfate reduction, to be a major community component. The model explains the high concentrations of acetate that are sometimes found in waters produced from water-injected oil fields.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".