Enhanced Swelling Effect and Viscosity Reduction of Solvents-CO2-Heavy Oil Systems
Bibliographic record
Abstract
Abstract In this paper, techniques have been developed to examine the enhanced swelling effect and viscosity reduction of CO2-saturated heavy oil with addition of rich solvent C3H8. Experimentally, PVT tests are conducted to measure the saturation pressure, swelling factor and viscosity of the C3H8-heavy oil system and C3H8-CO2-heavy oil system, respectively. It has been found that an increased swelling effect of heavy oil is obtained by adding rich solvent C3H8 into CO2 stream. An enhanced viscosity reduction of the CO2-heavy oil system is also achieved in the presence of rich solvents such as C3H8. Theoretically, two binary interaction parameter (BIP) correlations in Peng-Robinson equation of state (PR-EOS) have been proposed for respectively characterizing CO2-heavy oil systems and C3H8-heavy oil systems by treating each oil sample as a single pseudocomponent with its molecular weight and specific gravity. The BIP correlations together with the PR-EOS can be used to predict the saturation pressures and swelling factors of the C3H8-CO2-heavy oil systems with a good accuracy. Also, in comparison to other mixing rules, the Lobe’s mixing rule is found to be more appropriate for quantifying viscosity reduction of the heavy oil with dissolution of CO2 and/or C3H8.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".