Evaluating Diffusivity of Toluene in Heavy Oil Using Nuclear Magnetic Resonance Imaging
Bibliographic record
Abstract
Solvent-based processes have shown technical advantages over thermal techniques for recovery of heavy oil and bitumen. The success of these processes relies on accurate computation of molecular diffusion coefficient which determines how fast a solvent penetrates into oil. Concentration profile measurements of solvent in oil are used for the determination of the molecular diffusion coefficient. Although numerous experimental techniques have been proposed, the accurate estimation of this parameter is still a topic of debate in the literature. In this work, 1-D nuclear magnetic resonance imaging (MRI) is employed to obtain diffusivity data for a toluene–heavy oil system. Diffusion of toluene in heavy oil was monitored for 20 days at a controlled temperature of 35 °C and ambient pressure. Over time, toluene diffusion into oil leads to changes in spatial distribution of T 1 and T 2 that affect the received signal. This serves as the basis of the solvent and heavy oil concentration estimation. Consequently, concentration profiles were established by converting the MRI signals to concentration values. This conversion was achieved by creating samples with known concentrations of heavy oil–toluene and measuring their response in the same environment and parameter settings. A concentration-dependent diffusion coefficient was obtained from concentration profiles. The results show that relaxation based 1D MRI is an accurate and robust tool to obtain diffusivity data in complex fluids such as heavy oil.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".