Advances in Diffusivity Measurement of Solvents in Oil Sands
Bibliographic record
Abstract
Abstract This work deals with the prediction of mass transfer of solvents in bitumen in the presence and absence of sand through a unified model that uses magnetic resonance response information. Experiments were performed with pairs of solvents and bitumen in the presence of sand and without sand. Low field NMR was used to acquire spectra of the migrating solvent in bitumen or oil sand as a function of time. The experimental results of this work along with data collected previously on similar systems in our laboratory were fit together in a one dimensional Fickian model. The novelty of the presented approach is not only that the new model matches all the experiments to date, but also that this matching can be done independently and without external input parameters. In previous work matching could only be achieved if the depth of solvent penetration was provided through external measurements. The present model predicts diffusivities of solvents in bitumen in the presence of sand to be in the same order of magnitude and a bit lower than diffusivities of solvent in bulk bitumen. It is anticipated that this method would be of value when estimates of mass transfer in solvent based heavy oil processes are attempted in the field. Introduction In solvent-based recovery processes for heavy oil and bitumen, mass transfer phenomena compete with viscous forces, gravity and capillary forces as the predominant means for oil recovery or trapping. In order to understand the relative merit of such forces one must be able to measure mass transfer at reservoir conditions and potentially verify such measurements in the field. There is considerable literature on the measurement of recovery mechanisms and recovery efficiency through solvent based processes for heavy oil and bitumen. The bulk of the work presented in the recent literature focuses around the VAPEX (Vapor Extraction) process but the fundamental work presented can easily apply to any solvent displacement process. If we were to measure mass transfer phenomena in the field, it appears that a possible logging tool with significant potential is magnetic resonance. Thus our group has embarked in a project whereby magnetic resonance is used for the determination of bitumen (or heavy oil) solvent interactions. As two miscible fluids are in contact, they will slowly diffuse into each other. This molecular transport of one substance relative to another is known as diffusion. With time, the interface between the fluids will appear as a diffused mixed zone grading from one pure fluid to the other. The mechanism of diffusion happens due to the random motion of molecules(1,3). The diffusion flux between solvent and heavy oil can be defined as the flow due to concentration gradient between solvent and heavy oil. The diffusion phenomena take place when there is no mixing in the system and the only driving force is the concentration gradient, unlike dispersion that is caused due to flow of fluids in the porous medium. It was observed that as solvent and heavy oil diffuse into each other, the mobility of hydrogen bearing molecules of both oil and solvent change (4).
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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".