Simulation of sour‐oxic‐nitrite chemical environment in oil and gas facilities
Bibliographic record
Abstract
Abstract The nature of the chemical environment in oil and gas fluids such as produced water (PW) and soured oil or low‐oxygen environments plays a vital role in microbiologically influenced corrosion (MIC). H 2 S and/or other forms of sulphur species in soured oils and PW are key factors in the corrosion and growth of microorganisms. To mitigate reservoir souring and subsequent corrosion, nitrate is injected to displace sulphate‐reducing bacteria with nitrate reducers. However, nitrates and the associated nitrogen species (eg, nitrite) impact the chemistry and microbial activity, and hence the corrosion potential in the system. This study investigates the PW chemical environment in light of sulphide and nitrite chemistry, and provides information towards understanding the chemical transformations and microbial relationships. The sulphide‐nitrite environment was studied as a function of temperature, pressure, nitrite level, oxygen‐using equilibrium, and kinetic model approaches. Equilibrium simulation predicted the formation of FeS, FeO(OH), and Fe 2 O 3 as the key corrosion products, the amount of which varied depending on the chemistry and operating conditions. In experiments where nitrite was very low or absent, S 0 was favoured over as the inlet H 2 S concentration increased and FeS dominated with an increase in temperature. In the presence of nitrite, Fe 2 O 3 was formed instead of FeO(OH) at temperatures above 50°C. The trend of the kinetic simulation of the sulphide‐oxygen reaction in seawater was in good agreement with the wet‐lab experiment in PW. The models can serve as tools to better understand and describe the chemical environment in PW systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".