Experimental Measurement of Diffusion Coefficient of CO2 in Heavy Oil Using X-Ray Computed-Assisted Tomography Under Reservoir Conditions
Bibliographic record
Abstract
Abstract Injection of carbon dioxide has shown process and economical advantages for enhancing the heavy oil and bitumen recovery by reducing viscosity under the reservoir conditions. Mass transfer is the first mechanism to occur when carbon dioxide is injected into the reservoir. Consequently, the measurement and evaluation of the diffusion coefficient is essential to develop feasible and economic technology for extraction of heavy oil and bitumen. However, not much effort has been put into the experiments of carbon dioxide and heavy oil for understanding and calculation of the gas-liquid diffusion coefficient. The purpose of this study is to evaluate the feasibility of determining experimental diffusion coefficients of carbon dioxide in heavy oil by employing X-ray Computed Assisted Tomography (CAT) and a non-iterative finite volume method, and investigate the impact of different experimental conditions on diffusion coefficients. The results indicated that the measured carbon dioxide diffusion coefficients are consistent with those reported in the literature for similar gas-heavy oil systems. X-ray Computed Assisted Tomography (CAT) and a non-iterative finite volume method were successfully applied to study the diffusivity of carbon dioxide in heavy oil. In addition, the concentration and diffusion coefficients of carbon dioxide in heavy oil depend on diffusion distance as well as on diffusion time and pressure.
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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 | 0.001 | 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".