Dissolution and Mobilization of Bitumen at Pore Scale
Bibliographic record
Abstract
Abstract Steam injection, in various forms, has been commercially successful for in situ recovery from the oil sands. One of the recovery methods being tested – with an aim to reduce steam requirement – involves the injection of a suitable solvent, by itself, or in combination with steam. Solvents are being utilized at a pilot scale in both cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD) processes. The present study examines the mixing of a solvent with bitumen at the pore scale, using an analytical model with typical field data. The convective diffusion equation is solved for a spherical geometry, and applied to a drop of bitumen is a pore space. Calculations are done for a number of solvents, viz. pentane, and hexane. The rate of bitumen dissolution is determined for both static and dynamic conditions, for a number of velocities corresponding to typical field injection rates. The diffusion coefficients are taken from the latest laboratory data reported. Calculations are also done for heating of the bitumen (without solvent) for the same conditions. The resulting bitumen viscosity is compared as a function of time, for both solvent injection and heating. Results show that the time of solution of a bitumen drop in the solvent is of the order of days. The time varies with the type of solvent (largest for pentane), and the injection velocity. The times to attain the same viscosity by solvent injection in one case, and conduction heating in the other, respectively, differ by orders of magnitude. The significance of this finding for field application of solvents is discussed. The outcome of this research will improve our understanding about solvent diffusion and dispersion process for bitumen in porous media. This will help us to modify the current modelling approach to capture more realistic mass transfer and solvent/bitumen interaction in porous media.
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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".