Quantification of Gas Exsolution Dynamics for CO2/CH4-Heavy Oil Systems with Population Balance Equations
Bibliographic record
Abstract
Abstract Although foamy oil phenomenon has been considered as the key factor that dominates heavy oil recovery, the existing models cannot be used to accurately quantify gas exsolution dynamics in foamy oil under various conditions due to the inherent physics and complex flow behaviour. In this study, experimental and theoretical techniques have been developed to quantify gas exsolution dynamics of CO2/CH4-heavy oil systems while considering gas bubble nucleation mobilization, and binary coalescence. Experimentally, constant composition expansion (CCE) tests were performed with a sealed PVT apparatus for the CO2/CH4-heavy oil systems to induce foamy oil behaviour by gradually depleting pressure at a constant temperature, during which the pressures and volume changes were monitored and recorded continuously. Theoretically, the Fick's law, equation of state, classical nucleation theory, and population balance equation have been integrated to describe the gas exsolution dynamics, during which gas bubbles are discretized with the fixed-pivot technique. The gas bubble number and size distribution in the induced foamy oil can then be determined once the deviations between the measured and calculated parameters, including liquid volume and pseudo-bubble point pressure, have been minimized with the genetic algorithm. For both CO2- and CH4-heavy oil systems, not only can a reducing pressure depletion rate or an increasing temperature result in a higher pseudo-bubblepoint pressure, but also gas bubble growth is strongly dependent on both temperature and diffusion of a gas component in heavy oil, while increasing the solvent concentration in the heavy oil tends to hinder the gas bubble nucleation and mitigation due to the higher pressure set for the experiments. During the generation of foamy oil, a higher temperature reduces heavy oil viscosity to accelerate the diffusion process, positively contributing to the gas bubble nucleation, binary coalescence, and bubble mobilization, respectively. Compared with CO2, CH4 induces a stronger and more stable foamy oil, illustrating that, at a lower temperature, foamy oil is more stable with more dispersed gas bubbles. In this study, the newly developed theoretical techniques are able to reproduce gas exsolution dynamics at the bubble level, allowing us to seamlessly integrate them with any reservoir simulators to not only accurately characterize foamy oil behaviour, but also evaluate the associated recovery performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".