Oil-Viscosity Predictions From Low-Field NMR Measurements
Bibliographic record
Abstract
Summary Canada contains vast reserves of heavy oil and bitumen. Viscosity determination is key to the successful recovery of this oil, and low-field nuclear magnetic resonance (NMR) shows great potential as a tool for estimating this property. An NMR viscosity correlation previously had been developed that is valid for order-of-magnitude estimates over a wide range of viscosities and temperatures. This correlation was built phenomenologically, using experiments relating NMR spectra to viscosity. The present work details a more thorough investigation into oil viscosity and NMR, thus providing a theoretical justification for the proposed correlation. A novel tuning procedure is also presented, whereby the correlation is fitted using the Arrhenius relationship to improve the NMR viscosity estimates for single oils at multiple temperatures. Tuning allows for NMR to be potentially used in observation wells to monitor thermal enhanced oil recovery (EOR) projects or online to monitor the viscosity of produced-fluid streams as they cool.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".