Nitrate-reducing microorganisms prevent souring of an oil field produced water storage pond
Bibliographic record
Abstract
Nitrate addition for mitigating sulfide production in oil field systems has been studied in laboratory settings and in some subsurface oil reservoirs. To promote water recycling and reuse associated with oil reservoirs produced by hydraulic fracturing, high-salinity produced waters are temporarily stored in surface ponds prior to subsequent reinjection into the subsurface. In this study, nitrate was added directly to a storage pond to prevent sulfide accumulation. DNA sequencing of pond water over a 4-week period revealed a decrease in the proportion of sulfate-reducing microorganisms following nitrate application. Sulfate levels remained stable during this period, whereas nitrate and nitrite fluctuated in the days following the nitrate addition. Metagenome-assembled genomes (MAGs) reconstructed from the pond water microbiome highlighted different organisms with genes for organoheterotrophic and lithoheterotrophic nitrate reduction, whereas genes associated with sulfide production via sulfate or thiosulfate reduction were barely detected. Within those MAGs, genes for acetate metabolism were observed, consistent with acetate decreasing substantially in the pond water in the presence of nitrate. After nitrate was consumed an increase in relative abundance of putative autotrophic microorganisms was observed (e.g. Arhodomonas, Guyparkeria, and Psychroflexus), corresponding to a drop in total inorganic carbon measurements in the storage pond. This trial offers an overview on microbial processes taking place in storage pond environments in response to nitrate addition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".