Phase Behavior of Multicomponent Hydrocarbons in Organic Nanopores Under the Effects of Capillary Pressure and Adsorption Film
Bibliographic record
Abstract
Abstract The presence of nanopores in tight rocks and shales have been confirmed by numerous studies. These pores are also the primary storage space of oil and gas in shales. Due to the effect of nanoscale confinement, the phase behavior of fluids confined in such extra-low permeability formation (nanodarcy scale) differs significantly from those observed in the conventional formation. In this paper, the cubic PR EOR is coupled with the capillary-pressure equation and adsorption theory to investigate and represent the phase equilibria of pure components and their mixtures in cylindrical nanopores. The pore confinement effects of interaction between fluid molecule and pore wall and the shift of critical properties are all considered. Also due to the effect of adsorption film, an improved Young-Laplace equation is adopted to simulate the capillarity instead of the conventional equation. For the adsorption behavior, the experimental data on the adsorbent of silicalite are used to represent the adsorption behavior of hydrocarbons in nanopores. Then, a prediction process for the behavior of methane, n-butane, n-pentane, n-hexane and their mixtures are performed. And the results are compared against the available experimental data to confirm the accuracy of this scheme. The actual Eagle Ford oil is also used to examine the performance of our scheme. Results indicate that the presence of adsorption film could further increase the vapor-liquid equilibrium constant (K-value) and capillary pressure of the confined pure-component fluid, especially for the nanopores with few nanometers. The smaller the nanopore radius, the higher the deviation between the actual K-value and bulk value. For methane, when the pore radius is higher than about 20 nm, the K-value is approaching the bulk fluids and the effect of capillary pressure and adsorption film can be neglected. For n-pentane, it is about 18 nm. For binary mixture, it is found the higher the difference between the two components, the stronger the nanopore confinement effects. The capillary pressure will present a bilinear relation with the pore radius in the log-log plot. For multicomponent mixture, as the pore radius decreases, the bubble-point pressure is depressed, the dew-point pressure is increased, and the phase envelop of confined fluids is also shrinked. When the adsorption film is neglected, the bubble-point pressure will be overestimated, and the dew-point pressure is underestimated. For Eagle Ford oil, when the nanopore radius is higher than about 100 nm, the behavior will approach the bulk value and the influence of nanopore confinement can be neglected. This study will shed some important insights for the phase behavior of tight oil and gas condensate in nanopores.
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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".