Control of Microbial Sulfide Production with Biocides and Nitrate in Oil Reservoir Simulating Bioreactors
Bibliographic record
Abstract
Oil reservoir souring by the microbial reduction of sulfate to sulfide is unwanted, because it enhances corrosion of metal infrastructure used for oil production and processing. Reservoir souring can be prevented or remediated by the injection of nitrate or biocides, although injection of biocides into reservoirs is not commonly done. Whether combined application of these agents may give synergistic reservoir souring control is unknown. In order to address this we have used up-flow sand-packed bioreactors injected with 2 mM sulfate and volatile fatty acids (VFA, 3 mM each of acetate, propionate and butyrate) at a flow rate of 3 or 6 pore volumes (PV) per day. Pulsed injection of the biocides glutaraldehyde (Glut), benzalkonium chloride (BAC) and cocodiamine was used to control souring. Souring control was determined as the recovery time (RT) needed to re-establish an aqueous sulfide concentration of 0.8-1 mM (of the 1.7-2 mM before the pulse). Pulses were either for a long time (120 h) at low concentration (long-low) or for a short time (1 h) at high concentration (short-high). The short-high strategy gave better souring control with Glut, whereas the long-low strategy was better with cocodiamine. Continuous injection of 2 mM nitrate alone was not effective, because 3 mM VFA can fully reduce both 2 mM nitrate to nitrite and N2 and, subsequently, 2 mM sulfate to sulfide. No synergy was observed for short-high pulsed biocides and continuously injected nitrate. However, use of continuous nitrate and long-low pulsed biocide gave synergistic souring control with BAC and Glut, as indicated by increased RTs in the presence, as compared to the absence of nitrate. Increased production of nitrite, which increases the effectiveness of souring control by biocides, is the most likely cause for this synergy.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".