Monitoring Bitumen−Solvent Interactions with Low-Field Nuclear Magnetic Resonance and X-ray Computer-Assisted Tomography
Why this work is in the frame
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Bibliographic record
Abstract
This work involves the detection and monitoring of solvent interactions with heavy oil and bitumen. Two nondestructive methods low-field nuclear magnetic resonance (NMR) and X-ray computer-assisted tomography (CAT) were used. It is shown that low-field NMR can be a very useful tool in understanding the relationship of viscosity, density, and asphaltene precipitation in bitumen−solvent mixtures. Such mixtures are present in solvent-related heavy oil and bitumen recovery processes, such as vapor extraction (VAPEX). As a solvent comes into contact with a heavy oil or bitumen sample, the mobility of hydrogen-bearing molecules of both solvent and oil changes. These changes are detectable through changes in the NMR relaxation characteristics of both the solvent and the oil and can be correlated to mass flux and concentration changes. Based on Fick's second law, diffusion coefficients were calculated for combinations of three oils and six solvents. X-ray CAT scanning was also used in parallel for analysis of solvent diffusion into the bitumen. As the solvent was diffusing into the bitumen, a concentration gradient was obtained. Concentration values at certain times were used to calculate diffusion coefficients, which were compared with results obtained from NMR data, using both an analytical method and a numerical method. The diffusion coefficients were considered either as constants or as functions of solvent concentration in two models that have been developed during this research. The overall diffusion coefficients calculated for several pairs of oils and solvents at different ratios, both by NMR data and X-ray tomography, were on the order of 10 - 6 cm 2 /s.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it