Low-Field NMR Method for Bitumen Sands Characterization: A New Approach
Bibliographic record
Abstract
Summary The nuclear magnetic résonance (NMR) signal obtained from conventional oil, heavy oil, and bitumen formations can consist of both hydrocarbon and water signals. Each NMR signal can further characterize both mobile and immobile fluids in the porous media. However, as the viscosity of the hydrocarbon phase increases and the NMR signal shifts toward lower relaxation times, the composite NMR signal for the hydrocarbon-bearing formation becomes very complicated. As the viscosity of the bitumen exceeds 100,000 cp (at natural conditions), the relaxation characteristics of bitumen become partially nondetectable by both the logging and laboratory NMR tools. As a result, the conventional methods of NMR detection fail to precisely recognize the hydrocarbon components. Laboratory NMR measurements of bitumen-bearing porous media under different temperatures were performed. This method delivered new information about bitumen reserves in situ. The results show that low-field NMR holds promise for the characterization of recoverable heavy oil and bitumen reserves. This new approach can be applicable for real-time monitoring of thermal extraction, including monitoring the efficiency of thermal recovery methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".