Investigation of CO2 Diffusivity in Heavy Oil Using X-Ray Computer-Assisted Tomography Under Reservoir Conditions
Bibliographic record
Abstract
Abstract Since the 1950’s, the use of carbon dioxide to increase heavy oil recovery has attract more attention from industry and laboratory research. The injection of carbon dixode has shown technical and economical advantages for enhancing heavy oil and bitumen recovery, because it can effectively reduce viscosity under the reservoir conditions. When carbon dioxide is injected into the reservoir, it partially dissolves into the heavy oil and mass transfer is the first mechanism to occur. Consequently, the accurate prediction and evaluation of the diffusion coefficient of carbon dioxide in heavy oil is one of the key parameters to develop technology for extraction of heavy oil in a feasible and cost-effective way. However, few experimental data for diffusivity of carbon dioxide in heavy oil are available in the literature. Therefore, this study conducted in order to add to the existing the laboratory data for evaluation and calculation of diffusion coefficient of carbon dioxide into heavy oil. In the past, experimental methods used to determine the diffusion coefficient of a gas in heavy oil were conducted under a constant gas pressure, which assumed that oil phase can be contacted with infinite gas at a fixed pressure. In this study, by employing X-ray Computed Assisted Tomography (CAT) and a non-iterative finite volume method, the purpose is to evaluate and compare experimental diffusion coefficients of carbon dioxide in heavy oil under the constant pressure and decaying pressue at the same time. Moreover, investigation of impacts of pressure on diffusion coefficients is conducted. It is found that the diffusvity of carbon dioxide in heavy oil is sensitive to the system pressure. The comparison between carbon dioxide diffusion coefficients under the constant pressure and those measured under the decaying pressrue showed an obvious difference. The results of study are essential for understanding oil recovery through carbon dioxide injection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".