Two-Dimensional Magnetic Resonance Study of Synthetic Oil Sands
Bibliographic record
Abstract
Abstract Understanding oil sand structure and recovery processes at the laboratory scale is important for generating accurate models to predict performance ‘in the field’. Magnetic resonance imaging and nuclear magnetic resonance (NMR) are powerful nondestructive, non-invasive tools that have been shown to be ideal for characterizing porous materials. Previous work using NMR to investigate heavy oil and bitumen sands has primarily focused on one-dimensional relaxation analysis. Whilst the spectra produced are sensitive to the confining geometry and fluid types, there is often overlap between relaxation peaks from different fluids with similar relaxation times. These peaks are often difficult to separate accurately. We present a 20 MHz, 1H NMR two-dimensional correlation spectroscopy study of a range of synthetic oil sands systematically prepared with varying compositions of bitumen, water, sand and clay. These two-dimensional experiments couple nuclear spin relaxation or self-diffusion in one time period with relaxation or self-diffusion occurring during a subsequent time period. This results in the acquisition of proton population distributions as a function of longitudinal (T1) relaxation time and transverse (T2) relaxation time. This allows further, more accurate, discrimination of the fluids and their different physical environments within in the oil sands. The results can also potentially lead to improved wettability and viscosity analyses of heavy oils in-situ. The results from the synthetic samples are then applied to natural core samples.
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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 | 0.000 | 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) | 0.001 | 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".