Preliminary numerical modelling of CO<sub>2</sub> gas foaming in heavy oil and simulations of oil production from heavy oil reservoirs
Bibliographic record
Abstract
Basic understanding of numerical modelling for the effects of CO2 foaming on heavy oil production behaviour using the huff‐and‐puff process is relevant for CO2‐EOR performance. The numerical model was constructed based on laboratory measurements: CO2 solubility, foam swelling, and apparent viscosity. The model for unsaturated solubility in porous media, such as sandstone cores and oil reservoirs, was proposed by defining CO2 solubility in heavy oil for generating foamy oil. The foaming process was modelled with four kinds of foamy oils at discrete depressurizations below each equilibrium pressure. A numerical model of apparent foam viscosity with discrete depressurization was set up based on experimental measurements as a function of pressures and temperatures of 0.1–10 MPa and 20–50 °C. The matching between the numerical simulations of heavy oil drainage and experimental measurements of foaming in Berea sandstone cores (Psat = 10 MPa at 50 °C) shows 31 % oil recovery after depressurization to atmospheric pressure. The numerical simulation results of heavy oil production at field‐scale showed that CO2 gas production quickly increases after depressurization and then the foamy oil production increases following the peak gas production rate. In the first cycle of the huff‐and‐puff process, the maximum oil production rate ranged from 4–68 m3/day. From the sensitivity study it can be concluded that the initial oil saturation and the CO2 dissolution zone as compared with reservoir size play a main function in heavy oil production by CO2 gas foaming in the huff‐and‐puff 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".