Phase Behaviour and Viscosity Reduction of CO2-Heavy Oil Systems at High Pressures and Elevated Temperatures
Bibliographic record
Abstract
Abstract Techniques have been developed to experimentally and theoretically determine phase behaviour and viscosity reduction of CO2-heavy oil systems at high pressures and elevated temperatures. Experimentally, vapour-liquid phase boundaries (i.e., saturation pressure lines) and the swelling factors are measured by conducting PVT tests at pressures up to 11094.0 kPa and temperatures up to 362.75 K, respectively. The viscosity of CO2-saturated heavy oil is measured at 319.15 K. Theoretically, the heavy oil sample is respectively characterized as a single- and multi-pseudocomponent(s). An exponential distribution function is used to split the plus fraction of heavy oil up to C105+, while a logarithm-type lumping method is used to group the single carbon numbers (SCNs) into multiple pseudocomponents. Then, the Peng-Robinson equation of state (PR EOS) coupled with the modified alpha function is applied to quantify the phase and volumetric behaviour of the CO2-heavy oil systems. The binary interaction parameters (BIPs) for CO2-pseudocomponent(s) pair are tuned to match the measured saturation pressures. Compared with the characterization scheme of treating heavy oil as a single pseudocomponent, the absolute average relative deviation (AARD) for the predicted saturation pressures can be improved from 5.27% to 4.56% by characterizing the heavy oil as six pseudocomponents. With the optimum BIPs, the swelling factors are predicted by the PR EOS with and without the volume translation technique, respectively. It is found that the introduction of the volume shift to each (pseudo)component in the PR EOS is able to provide more accurate prediction in both characterization schemes with AARD of 1.88% (oil as a single pseudocomponent) and 1.39% (oil as six pseudocomponents), respectively.
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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 | 0.000 |
| 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".