Quantification of Mutual Mass Transfer of CO<sub>2</sub>/N<sub>2</sub>–Light Oil Systems by Dynamic Volume Analysis
Bibliographic record
Abstract
A novel and pragmatic method has been developed to quantify mutual mass transfer between a gas (e.g., CO2 and N2) and light oil by dynamic volume analysis. Experimentally, diffusion experiments for pure CO2/N2-light oil systems are conducted at a constant pressure and temperature with a pressure/volume/temperature (PVT) system. During the diffusion experiments, the diluted volume of oil phase is continuously monitored and recorded, while gas samples are collected at the end of the diffusion tests to measure gas compositions by performing gas chromatography (GC) analysis. Theoretically, the mass transfer from light oil to gas phase and the solubility of a gas into the light oil is quantified by combining the GC analysis and Fick’s second law, while the Peng–Robinson equation of state is employed to determine the concentration of each gas component in the oil phase by imposing the quasi-equilibrium boundary condition at the gas–oil interface. The diffusion coefficient of each component can be determined once the discrepancy of both the swelling factor and the gas composition between the experimental measurements and the calculated ones are minimized. At temperature of 336.15 K, the diffusion coefficients of carbon dioxide and nitrogen are determined to be 12.87 × 10–9 m2/s at pressure of 2170 kPa and 1.35 × 10–9 m2/s at pressure of 5275 kPa, respectively. For the diffusion coefficient of light oil to the gas phase, it is determined to be 6.04 × 10–11 m2/s for the CO2-light oil system and 2.55 × 10–12 m2/s for the N2-light oil system under the corresponding conditions. Furthermore, the dynamic swelling factor of oil in the CO2-light oil system is measured to be higher than that of the N2-light oil system. The GC analysis confirms that there exists light-component extraction, though the concentration is small in this study since the experimental pressure is below the critical pressure of gas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".