Vapor-Liquid Equilibria and Diffusion of CO2/<i>n</i>-Decane Mixture in the Nanopores of Shale Reservoirs
Bibliographic record
Abstract
Abstract Numerous laboratory tests on the Northern American shale plays have observed a large number of nanopores. Because of the pore-proximity effect, the vapor-liquid phase equilibrium and transport performance of fluids in nanopores differ significantly from that observed in PVT cell. In recent years, CO2 huff-and-puff has been widely applied to unlock the shale reservoirs. But on account of the high adsorption selectivity of CO2, after the injection of CO2, the original vapor-liquid equilibria of hydrocarbons is changed. The purpose of this study is to predict the phase behavior and diffusion of the CO2/n-decane mixtures in the nanopores. The Peng-Robinson (PR) equation of state is combined with Young-Laplace equation to calculate the phase-composition diagram at the presence of capillary pressure. The equilibrium molecular dynamics simulations (MDS) are also conducted to study the phase behavior, and the number density profiles of different molecules are calculated. Then, based on the discussion of phase behavior, a series of equilibrium MDS runs are carried out to calculate the self-diffusion coefficients of CO2, n-decane, and all fluid molecules. For each MDS with a different CO2 mass fraction, the two types of fluid molecules are thoroughly mixed, the conditions of pore size and temperature are consistent with those in the phase behavior studies. Results indicate that considering the capillary pressure, when the mass fraction of CO2 is less than 40%, the bubble point suppression is more clearly shown in the phase envelope. The number density profiles of n-decane molecules show the apparent characteristics of adsorption layers. As the mass fraction of CO2 molecules increases, the self-diffusion coefficients of CO2, n-decane, and their mixtures all increase. The self-diffusion coefficients of CO2 molecules are higher than that of the n-decane molecules, and the diffusion coefficients of the entire fluid system are somewhere in between. Appropriate CO2 injection into shale oil reservoirs can not only reduce the confinement-induced bubble point suppression but also improve the flow behavior of oil in nanopores. This study can shed some critical insights for the vapor-liquid phase equilibria of confined fluids in nanopores and provide sound guidelines for the application of CO2 huff and puff in shale reservoirs.
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".