The Effectiveness of Nitrate-Mediated Control of the Oil Field Sulfur Cycle Depends on the Toluene Content of the Oil
Bibliographic record
Abstract
The injection of nitrate is one of the most commonly used technologies to impact the sulfur cycle in subsurface oil fields. Nitrate injection enhances the activity of nitrate-reducing bacteria (NRB), which produce nitrite inhibiting sulfate-reducing bacteria (SRB). Subsequent reduction of nitrate to di-nitrogen (N2) alleviates the inhibition of SRB by nitrite. It has been shown for the Medicine Hat Glauconitic C (MHGC) field, that alkylbenzenes especially toluene are important electron donors for the reduction of nitrate to nitrite and N2. However, the rate and extent of reduction of nitrate to nitrite and of nitrite to nitrogen have not been studied for multiple oil fields. Samples of light oil (PNG, CPM and Tundra), light/heavy oil (Gryphon and Obigbo) and of heavy oil (MHGC) were collected from locations around the world. The maximum concentration of nitrate in the aqueous phase, which could be reduced in microcosms inoculated with MHGC produced water, increased with the toluene concentration in the oil phase. PNG, Gryphon, CPM, Obigbo, MHGC and Tundra oils had 77, 17, 5.9, 4.0, 2.6 and 0.8 mM toluene, respectively. In incubations with 49 ml of aqueous phase and 1 ml of oil these were able to reduce 22.2, 12.3, 7.9, 4.6, 4.0 and 1.4 mM of nitrate, respectively. Nitrate reduced increased to 35±4 mM upon amendment of all these oils with 570 mM toluene prior to incubation. Souring control by nitrate injection requires that the nitrate is directed towards oxidation of sulfide, not toluene. Hence, the success of nitrate injections will be inversely proportional to the toluene content of the oil. Oil composition is therefore an important determinant of the success of nitrate injection to control souring in a particular field.
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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) | 0.001 | 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".