Understanding water soluble organics in upstream production systems
Bibliographic record
Abstract
Abstract A mathematical model has been developed which allows correlation and prediction of chemical and phase equilibrium of dissolved organic components in produced water. The model is applied primarily to prediction of results from the EPA-1664 method of analysis. The model provides a clear understanding of the contributions of dissolved organics, dispersed oil, and the effect of pH on the so-called Water Soluble Organics. This can be applied to understand the consequences of reducing temperature and pressure that typically occurs in the production train, and the increase in pH which typically accompanies release of CO2 into the gas phase. The model does not predict the formation or resolution of oil-in-water emulsions or dispersions. Those variables, important in the application of the model, are assumed to be measured or calculated and available for input into the present model. The model is also applied to sampling and analysis, which is an important part of processing produced water containing dissolved organics. The main objective of the model is to help select better processes to remove dissolved organics, and to explain the role of sampling and analysis meeting discharge regulations. It is demonstrated that deep removal of dispersed organics can result in lowering of both total oil and grease and water soluble organics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".