A comparison of PAHs in produced water discharges and flared gas emissions to the ocean
Bibliographic record
Abstract
Produced water represents the single largest source of marine pollution in terms of toxicity and volume from an offshore platform in the production stage of oil and gas operations. In the past few years increasing attention has focused on the presence and possible impact of polycyclic aromatic hydrocarbons (PAH) in produced water. Studies have shown that the concentration of the 16 EPA PAHs can vary from 0.7 to 100s mg/L in produced water. However, particularly on oil platforms, the flaring of produced gas can also be a significant source of PAHs. A study by the Alberta Research Council in 1996 identified many PAHs in the emissions from the flared produced gas (up to 300 mg/m/sup 3/ for the 16 EPA PAHs). It is likely that much of the PAHs in the flared emissions will end up in the water and so should be assessed against the produced water input of PAHs. However, it is difficult to compare concentrations in the flared emissions and produced water outfall directly as the dispersion in each media, characteristics of the flare stack and outfall, and meteorological conditions are all factors in mass input into the water. In this study the concentration of PAHs in the emissions was converted to an emission rate and input into an air dispersion model. The maximum ground level concentrations were determined with the model and then converted to a seawater concentration using a simple equilibrium model. The produced water outfall was simulated using CORMIX and the resulting concentrations were compared with the air dispersion model results.
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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 | 0.001 |
| 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".