Comparison of Nitrate and Perchlorate in Controlling Sulfidogenesis in Heavy Oil-Containing Bioreactors
Bibliographic record
Abstract
Control of microbial reduction of sulfate to sulfide in oil reservoirs (a process referred to as souring) with nitrate has been researched extensively. Nitrate is reduced to nitrite, which is a strong inhibitor of sulfate-reducing bacteria (SRB). Perchlorate has been proposed as an alternative souring control agent. It is reduced to chlorate (ClO3-) and chlorite (ClO2-), which is dismutated to chloride and O2. These can react with sulfide to form sulfur. Chlorite is also highly biocidal. Here we compared the effectiveness of perchlorate and nitrate in inhibiting SRB activity in medium containing heavy oil from the Medicine Hat Glauconitic C (MHGC) field, which has a low reservoir temperature and is injected with nitrate to control souring. Using acetate, propionate and butyrate as electron donors, perchlorate-reducing bacteria (PRB) were obtained in enrichment culture and perchlorate-reducing Magnetospirillum spp. were isolated from MHGC produced waters. In batch experiments with MHGC oil as the electron donor, nitrate was reduced to nitrite and inhibited sulfate reduction. However, perchlorate was not reduced and did not inhibit sulfate reduction in these incubations. Bioreactor experiments were conducted with sand-packed glass columns, containing MHGC oil and inoculated with an oil-grown mesophilic SRB enrichment. Once active souring (reduction of 2 mM sulfate to sulfide) was observed, these were treated with nitrate and/or perchlorate. As in the batch experiments, 4 mM nitrate completely inhibited sulfide production, while partial inhibition occurred with 1 and 2 mM nitrate, but injection of 4 mM perchlorate did not inhibit sulfate reduction and perchlorate was not reduced. The enriched and isolated PRB were unable to use heavy oil components, like alkylbenzenes, which were readily used by nitrate-reducing bacteria. Hence perchlorate, injected into a low temperature heavy oil reservoir like the MHGC, may not be reduced to toxic intermediates making nitrate a preferable souring control agent.
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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) | 0.000 | 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 | 0.001 | 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".