Applications of Low Field NMR Techniques in the Characterization of Oil Sand Mining, Extraction and Upgrading Processes
Bibliographic record
Abstract
A number of techniques have previously been developed that use low field nuclear magnetic resonance (NMR) relaxometry for conventional and heavy oil reservoir characterization. In the current work, the adaptation of these algorithms for use in the oil sands industry is presented. NMR based methods have been developed for identification of water and bitumen content in ore and froth samples. Consistent algorithms have been used to analyze over 500 ore samples and 50 froth samples from the Athabasca oil sands in northern Alberta. Preliminary analyses are shown, with applications for in-situ fluid determination using NMR logging tools and improved process control in oil sands processing plants. Plusieurs techniques reposant sur la relaxométrie à résonance magnétique nucléaire (RMN) pour la caractérisation des réservoirs conventionnels et d'huiles lourdes ont été mises au point antérieurement. Dans le présent travail, on présente l'adaptation de ces algorithmes à des fins d'utilisation dans l'industrie des sables bitumineux. Des méthodes reposant sur la RMN ont été mises au point pour la détermination de la teneur en eau et en bitume dans des échantillons de minerai et d'écume. On a utilisé des algorithmes consistants pour analyser plus de 500 échantillons de minerai et 50 échantillons d'écume venant des sables bitumineux d'Athabasca dans le nord de l'Alberta. Des analyses préliminaires sont présentées dans le cadre de la détermination des fluides in situ au moyen de sondes de RMN et d'une régulation de procédé amélioré dans les usines de traitement des sables bitumineux.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".