Application of Gibbs Ensemble Monte Carlo to Phase Equilibria of CO2/Hydrocarbon Mixtures
Bibliographic record
Abstract
Abstract Gibbs Ensemble Monte Carlo (GEMC) is a molecular simulation method to predict phase behavior of fluids, such as crude oil and natural gas. It enables us to visualize microscopic structures of fluid phases. In this study, we apply GEMC to phase behavior of CO2/oil systems, where the CO2-rich liquid (L2) phase can coexist with the oil-rich liquid (L1) phase and vapor (V) phase. The L2 phase can have comparable density as the L1 phase. When the L2 phase is dense enough to extract light and intermediate hydrocarbons, CO2 flooding can achieve high displacement efficiency of more than 90%. A clear understanding of the L2 phase will help us design CO2-solvent injection processes. However, little is known about its microscopic structures and corresponding dynamic properties (i.e., viscosities and diffusion coefficients), apart from densities and phase compositions. The GEMC method is used to calculate phase equilibria of CO2/C16H34 mixtures at different pressures at 305 K, which are close to the critical point of CO2. Vapor-liquid phase equilibrium is predicted at pressures lower than 75 bar and liquid-liquid phase equilibrium at higher pressures up to 170 bar, where the density of the L2 phase is around 0.868 g/cm3 and that of L1 phase around 0.871 g/cm3. These results are in good agreement with the previous experimental data. By further adding CH4 and C2H6 into the mixture, liquid/liquid/vapor equilibrium was observed. The mole fraction, density and structural properties such as pair distribution function (which is directly related to the X-Ray diffraction), molecular clustering, and aggregation status of the three different phases are presented. Currently, we are extending our calculations to other CO2/hydrocarbon systems and calculating viscosities of two different liquid phases using Molecular Dynamics simulations with the atomic model used for GEMC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".