Low Field NMR Water Cut Metering
Bibliographic record
Abstract
Abstract We have developed a new on line water cut meter using low field Nuclear Magnetic Resonance (NMR) technology. This instrument is designed for use on heavy oil systems where conventional instruments experience difficulties. We present laboratory and field data for application of low field NMR to water cut measurements of bitumen/water mixtures. Data from successful field tests near Cold Lake, Alberta, Canada, shows that the instrument is capable of making water cut measurements over a wide range of fluid types and temperatures. We have successfully measured fluid streams with temperatures ranging from 60 to 150 degrees Centigrade and with water cut ranging from 40 to 95 percent. The instrument is capable of functioning accurately over a wide range of emulsions and/or foams and through significant variations of water salinity. The current application is for water cut measurements on a well site. However, the instrument can be applied in any system where heavy oil, bitumen, water, gas and solid systems may be encountered. It can be used for water cut and/or for three phase (oil/water/gas) volume fraction measurements. The instrument is equally capable of performing well site monitoring for regulatory/reconciliation purposes, for characterizing produced fluids, in separation, pipelining and upgrading processes for process control and for quality testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".