Novel Application of Nitrate as H2S Control Strategy in Permian Basin Produced Water Storage Ponds
Bibliographic record
Abstract
Abstract Produced water (PW) ponds are important facilities for supporting hydraulic fracturing in the Permian Basin. The control of H2S in these facilities is critical to safe and reliable frac and production operations. Effective microbial control strategies are required to mitigate sulfate-reducing bacteria (SRB) activity and fouling of production facilities with iron sulfide. Next generation sequencing (NGS) DNA analyses of Delaware Basin produced water (PW) samples highlighted a bacterial consortium dominated by putative halophilic fermentative and sulfate-reducing bacteria such as Halanaerobium spp. and Desulfohalobium spp. Anaerobic biodegradation of hydrocarbons can generate metabolites which serve as electron donors to support SRB activity. The novel application of calcium nitrate to produced water storage ponds for SRB control was piloted in Delaware Basin. DNA analysis demonstrated the impact of nitrate on the microbial consortium in the treated pond. Putative nitrate-reducing bacteria became dominant, with a greatly reduced abundance of SRB. Produced water bacterial growth experiments demonstrated the controls of redox potential and salinity on bacterial nitrate reduction, to aid in interpretation of the field data. The pilot was effective in preventing biogenic sulfidogenesis and has been adopted as a long-term mitigation strategy in PW storage ponds.
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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".