Sulfide Remediation by Pulsed Injection of Nitrate into a Low Temperature Canadian Heavy Oil Reservoir
Bibliographic record
Abstract
Sulfide formation by oil field sulfate-reducing bacteria (SRB) can be diminished by the injection of nitrate, stimulating the growth of nitrate-reducing bacteria (NRB). We monitored the field-wide injection of nitrate into a low temperature (approximately 30 degrees C) oil reservoir in western Canada by determining aqueous concentrations of sulfide, sulfate, nitrate, and nitrite, as well as the activities of NRB in water samples from 3 water plants, 2 injection wells, and 15 production wells over 2 years. The injection water had a low sulfate concentration (approximately 1 mM). Nitrate (2.4 mM, 150 ppm) was added at the water plants. Its subsequent distribution to the injection wells gave losses of 5-15% in the pipeline system, indicating that most was injected. Continuous nitrate injection lowered the total aqueous sulfide output of the production wells by 70% in the first five weeks, followed by recovery. Batchwise treatment of a limited section of the reservoir with high nitrate eliminated sulfide from one production well with nitrate breakthrough. Subsequent, field-wide treatment with week-long pulses of 14 mM nitrate gave breakthrough at an additional production well. However, this trend was reversed when injection with a constant dose of 2.4 mM (150 ppm) was resumed. The results are explained by assuming growth of SRB near the injection wellbore due to sulfate limitation. Injection of a constant nitrate dose inhibits these SRB initially. However, because of the constant, low temperature of the reservoir, SRB eventually grow back in a zone further removed from the injection wellbore. The resulting zonation (NRB closest to and SRB further away from the injection wellbore) can be broken by batch-wise increases in the concentration of injected nitrate, allowing it to re-enter the SRB-dominated zone.
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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) | 0.000 | 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".