The influence of nitrate on microbial processes in oil industry production waters
Bibliographic record
Abstract
Sulfide accumulation due to bacterial sulfate reduction is responsible for a number of serious problems in the oil industry. Among the strategies to control the activity of sulfate-reducing bacteria (SRB) is the use of nitrate, which can exhibit a variety of effects. We investigated the relevance of this approach to souring oil fields in Oklahoma and Alberta in which water flooding is used to enhance oil recovery. SRB and nitrate-reducing bacteria (NRB) were enumerated in produced waters from both oil fields. In the Oklahoma field, the rates of sulfate reduction ranged from 0.05 to 0.16 microM S day(-1) at the wellheads, and an order of magnitude higher at the oil-water separator. Sulfide production was greatest in the water storage tanks in the Alberta field. Microbial counts alone did not accurately reflect the potential for microbial activities. The majority of the sulfide production appeared to occur after the oil was pumped aboveground, rather than in the reservoir. Laboratory experiments showed that adding 5 and 10 mM nitrate to produced waters from the Oklahoma and Alberta oil fields, respectively, decreased the sulfide content to negligible levels and increased the numbers of NRB. This work suggests that sulfate reduction control measures can be concentrated on aboveground facilities, which will decrease the amount of sulfide reinjected into reservoirs during the disposal of oil field production waters.
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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 | 0.001 |
| 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 | 0.001 | 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".