Oil Field Biotechnology: Should We Use Nitrate or Nitrite to Remediate Souring
Bibliographic record
Abstract
Abstract The application of nitrate or nitrite to remediate souring during waterflooding is proven technology with the objective of (i) removing sulfide produced by sulfatereducing bacteria (SRB) and (ii) inhibiting further sulfide production by the SRB. Nitrate or nitrite injection stimulates heterotrophic, nitrate-reducing bacteria (hNRB) and nitrate-reducing, sulfide oxidizing bacteria (NR-SOB). In fields with SRB, but little or no hNRB and NR-SOB activity the use of nitrite is preferable, because nitrite directly inhibits SRB. Nitrate has no effect under these conditions. In fields with all 3 bacterial groups use of nitrate is preferred, because it has more oxidative power. The nitrate dose required is then determined largely by the concentration of degradable oil organics. Unfortunately this concentration is generally unknown. Introduction Oil fields that are subject to waterflooding can experience SRB-mediated hydrogen sulfide production over time (souring), especially when sea water wich has a high sulfate content water (∼30 mM) is used for injection For instance in 1994 total H2S production from the Skjold field in the Danish sector of the North Sea was 100 kg/day, whereas in 2000 this had increased to 700 kg/day, with occasional surges to 1100 kg/day(1). Because sulfide is toxic and corrosive it needs to be removed by chemical treatment e.g. amine scrubbing. A second negative aspect of souring is that it can reduce reservoir permeability by precipitating soluble metal ions as the sulfides. Souring can be reduced by biocide treatment. Supplementing injection water in the Skjold field periodically with tetrakishydroxymethylphosphonium sulfate (THPS) caused dips in H2S production. In the Velsefrikk field in the Norwegian sector of the North Sea glutaraldehyde was injected biweekly from 1989 to 1999(2). In Alberta and Saskatchewan oil reservoirs diamines are used routinely to reduce souring and corrosion associated with SRB growth in topsides equipment. Instead of using toxic biocides, the use of nitrate or nitrite has been advocated as a less hazardous, environmentally friendly alternative. The application of nitrates and their mechanism of action are discussed in this paper. EXAMPLES OF APPLICATION OF NITRATES The use of nitrates to reduce souring in offshore operations in the North Sea is well-documented. In the case of the Velsefrikk field biocides were completely replaced with continuous injection of 30 ppm nitrate since 1999, resulting in decreased souring and corrosion(2). Injection of 150 to 250 ppm nitrate for 3 months in the Skjold field was partially successful with 80% sulfide removal from fractured areas and lower levels of sulfide removal from areas of reduced permeability(1). In view of this limited success the use of THPS was resumed. Use of nitrite, instead of nitrate, gave successful souring control of gas wells for up to 7 months and of oil wells for up to 1 month following treatment(3). It was claimed that oil production increased following nitrite treatment due to dissolution of solid sulfides that were blocking the injection flow path(3). Closer to home injection of 400 ppm nitrate reduced sulfide concentrations in injector and producing wells by 73% in a field test in the Coleville field near Kindersley (SK)(4).
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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 | 0.004 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".