Equilibrium or Nonequilibrium Models: A Critical Issue in Determination of Gas Diffusivity in Oil
Bibliographic record
Abstract
Abstract Accurate simulation, design and implementation of any gas injection and solvent-based oil recovery processes requires a thorough understanding of a gas diffusion coefficient. This parameter is usually determined by monitoring gas pressure in a PVT cell during a pressure-decline test and analyzing the data by developing mathematical models that are different in imposed boundary conditions at a gas-liquid interface (the so-called equilibrium or nonequilibrium boundary conditions). Classically, the equilibrium boundary condition was applied at the gas-oil interface with the assumption of saturated interface at all times. But, recently in a number of researches it has been claimed that some time is required before establishment of the equilibrium state at the interface. These researchers considered existence of the non-equilibrium state and used a Robin-type boundary condition to account for the effect of resistance at the gas-oil interface on gas diffusion into oil. This paper considers these boundary conditions and investigates the effects of taking into account nonequilibrium nature of a diffusion process on estimation of mass transfer parameters using a lot of literature pressure-decay data of CH4, C2H6, CO2 and N2 dissolution in Athabasca bitumen at two different temperatures (50 and 90 °C) and an initial pressure of 8 MPa. Mass transfer parameters (a diffusion coefficient and a mass-transfer coefficient) are individually determined for each test by means of the equilibrium and non-equilibrium models and compared with each other. It is concluded that the values of the diffusion coefficient calculated by the equilibrium and non-equilibrium models are in an excellent agreement, indicating sufficiency of the classical mass transfer models to estimate the diffusion coefficient. On the other hand, although the non-equilibrium effects exist during gas dissolution in oil, applying a Robin-type boundary condition at the two-phase interface is not significant enough to affect the gas diffusion process.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".