Applications of Low Field Magnetic Resonance in Viscous Crude Oil/Water Property Determination
Bibliographic record
Abstract
The application of low field nuclear magnetic resonance (NMR) for reservoir petrophysics dates back to the 1950s and 1960s. In these early applications, the goal was to measure the total fluid present and to gain an understanding of the viscosity of the fluid based on the fluid NMR relaxation times. The increase in low field NMR usage in petrophysics during the 1990s also corresponded with the time oil producers were also focusing more heavily on production of heavy oil and bitumen. This chapter focuses on interpretation of low field NMR relaxation distributions in heavy oil and bitumen systems. It also studies the NMR response in mixtures of oil and water and diluted oils with solvents, with the goal of measuring fluid content and fluid properties in heavy oil systems. The key to measuring oil and water cuts using NMR is that the fluids have different viscosities and this leads to distinctly different NMR relaxation times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.003 |
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".