Selection, Operation, and Evaluation of High Temperature Oil in Water Monitor for Two-Phase Extra Heavy Oil (Bitumen) Test Separator Service at Peace River
Bibliographic record
Abstract
Abstract Since 1986, Shell Canada has been using two-phase separators for well test application in bitumen production service. As bitumen (8 to 10 API) at Peace River has a density similar to that of produced water, depending on operating temperature, the use of three-phase test separators is not practical. In bitumen service, a three-phase separator requires either heating or cooling plus diluent to reduce the bitumen density so that classical gravity separation can be achieved. With a two-phase system, a means is required to determine the amount of oil in the produced emulsion. This is often achieved by either obtaining a representative fluid sample from the separator and determining the water cut in the lab, or by an online instrument. The two-phase well test system operates at temperatures ranging from 80 to 200 °C at an operating pressure of 1500 kPa. The water cuts from the wells range from 10 to 90 percent with an average of 40 percent. Agar's OW-201 water-cut monitors, in conjunction with Coriolis mass flow meters, were selected for test separator systems at Peace River. This paper describes the field tests performed to verify the Agar OW-201 meter operation for Peace River service, Shell's operating experience to date and the performance achieved during one year of operation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".