Ammonium Concentrations in Produced Waters from a Mesothermic Oil Field Subjected to Nitrate Injection Decrease through Formation of Denitrifying Biomass and Anammox Activity
Bibliographic record
Abstract
Community analysis of a mesothermic oil field, subjected to continuous field-wide injection of nitrate to remove sulfide, with denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA genes indicated the presence of heterotrophic and sulfide-oxidizing, nitrate-reducing bacteria (hNRB and soNRB). These reduce nitrate by dissimilatory nitrate reduction to ammonium (e.g., Sulfurospirillum and Denitrovibrio) or by denitrification (e.g., Sulfurimonas, Arcobacter, and Thauera). Monitoring of ammonium concentrations in producing wells (PWs) indicated that denitrification was the main pathway for nitrate reduction in the field: breakthrough of nitrate and nitrite in two PWs was not associated with an increase in the ammonium concentration, and no increase in the ammonium concentration was seen in any of 11 producing wells during periods of increased nitrate injection. Instead, ammonium concentrations in produced waters decreased on average from 0.3 to 0.2 mM during 2 years of nitrate injection. Physiological studies with produced water-derived hNRB microcosms indicated increased biomass formation associated with denitrification as a possible cause for decreasing ammonium concentrations. Use of anammox-specific primers and cloning of the resulting PCR product gave clones affiliated with the known anammox genera "Candidatus Brocadia" and "Candidatus Kuenenia," indicating that the anammox reaction may also contribute to declining ammonium concentrations. Overall, the results indicate the following: (i) that nitrate injected into an oil field to oxidize sulfide is primarily reduced by denitrifying bacteria, of which many genera have been identified by DGGE, and (ii) that perhaps counterintuitively, nitrate injection leads to decreasing ammonium concentrations in produced waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".