Bibliographic record
Abstract
Abstract In many mature heavy-oil fields, production well-testing is limited by the ability of test separators to separate gas and water from heavy viscous oil. The new technology of multiphase metering addresses these issues through the use of a low cost, robust Multi-Phase Flow Meter (MPFM). This paper describes the fundamentals and field tests of this low-cost, portable multiphase meter. The meter utilizes a new coriolis flow meter technology combined with a microwave-based water cut meter that can measure 0-100% water-cut in the 0-100% Gas Void Fraction (GVF) range. The combination of these technologies provides a light-weight metering package that can be mounted onto trailers and/or pick-up trucks for portable well testing. This multiphase meter can measure oil, water and gas without separation of the production stream at low & high GORs (Gas-to-Oil Ratio). These applications can be found in mature fields where the conventional test separators are inefficient or under-sized for heavy oil applications. In these types of applications, a low-cost, accurate multiphase flow meter offers many benefits – one of which is accurate well test data for production optimization. The multiphase meter was subjected to qualifying tests prior to deployment in oil fields in the U.S., Alberta, Canada, Surinam, Venezuela, Romania and other locations where heavy oil rules out conventional "Sputniks" and other type of gravity separators. The paper describes the new fundamentals of the patented technology showing actual test 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".