High-Temperature Multiphase Flowmeters in Heavy-Oil Thermal Production
Bibliographic record
Abstract
Abstract The accurate measurement of Oil, Water and Gas/Steam in heavy oil thermal production (SAGD and other Steam Flood Processes) is a very difficult task faced by the heavy oil industry. The accuracy of these measurements is critical for reservoir management and production diagnostics. Mulitphase flow meter technology has been used successfully around the world for over 10 years and in heavy oil "cold" production in Venezuela and other countries. But multiphase technology has never been used in Extra Heavy Oil Thermal Production. The Canadian heavy oil thermal producers regularly see production temperatures exceeding 200 C (392 F) and some wells are approaching 232 C (450 F). New technology is required to accurately measure wells producing at these elevated temperatures. The first field tests using a multiphase flow meter in a heavy oil thermal project was conducted by one of the major Canadian producers in the fall of 2004. Additional tests were completed during the summer of 2005 with another heavy oil producer. This paper will review the unique problems encountered with testing heavy oil in high temperature applications. The test results from multiple well tests and the accuracy of the multiphase flow meters when compared to the field reference will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".