Oil Sands wettability characterization using low field nuclear magnetic resonance
Bibliographic record
Abstract
Wettability is a profoundly important parameter need to be understood in reservoir engineering. It has direct impact on the nature of fluid trapping, residual oil saturations, and mechanisms of displacement at the pore scale. Unfortunately, this parameter is very difficult to measure in unconsolidated systems, such as the oil sands of northern Alberta. Furthermore, in oil sands where the oil viscosity is much higher than that of water, conventional Amott/USBM testing cannot be applied. Therefore alternative technologies should be considered. Previous researches have shown that NMR technology could be used for wettability characterization. This study systematically investigated the NMR signal variation trend on well characterized model samples under different wettability conditions, as well as the effect of viscosity of oil phase on NMR signal variation. A better understanding on the effects of wetting conditions and saturation on NMR T 2 relaxation variation were obtained for both water and oil phase. Based on these findings, the wettability condition derivations from fluid NMR signal distribution analysis were explained. Furthermore, the effect of connate water on oil sands wettability as well as the bitumen recovery from oil sands was also investigated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".