Advances in Oil and Water Saturation Measurements Using Low Field NMR
Bibliographic record
Abstract
Abstract Low field nuclear magnetic resonance (NMR) relaxometry has been successfully used in the past to perform in-situ estimates of oil and water content in unconsolidated oil sand samples. This work has intriguiging applications in the oil sands mining and processing industry, in the areas of ore and froth characterization. Studies have previously been performed on a database of ore and froth samples from the Athabasca region in northern Alberta, and preliminary results have been encouraging. In this paper, supporting data is presented and refinements suggested to the previous algorithms, to improve the oil and water saturation predictions. A suite of real and synthetic samples of bitumen, water, clay and sand have been used to investigate the physical interactions of the different components on the NMR spectra. An automated algorithm is used to separate the oil and water NMR signals, and this algorithm is tested against samples both from this zone and from other heavy oil fields. Moreover, preliminary observations regarding spectral properties indicate that it may be possible in the future to estimate the amount of clay in the samples, based upon shifts in the NMR spectra. NMR estimates of oil and water content are fairly accurate, thus enhancing the possibility of using NMR for both in-situ oil sands development and in the oil sands mining industry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".