Simulation of flow and heat transfer in high‐temperature and high‐pressure reservoir based on multi‐physical field coupling model at pore scale
Bibliographic record
Abstract
Abstract The use of irregular pore‐scale models to study heavy oil reservoirs with high‐temperature, high‐pressure, and high‐stress characteristics is effective. Previous studies have typically focused on regular models and conventional environmental reservoirs, with limited exploration of irregular models and reservoirs in extreme environments. In investigating the process of water displacing heavy oil within reservoirs under high‐temperature, high‐pressure, and high‐stress conditions at the pore scale, the utilization of the four‐parameter method creates a micro‐scale irregular porous media model. The model systematically considers the variation of physical properties of rocks and heavy oil with temperature. The results indicate that an appropriate increase in water injection rate or a decrease in reservoir contact angle will increase the recovery rate, temperature, and stress of the reservoir. At a displacement time of 0.3 s, with the water injection rate increasing from 0.004 to 0.01 m ∙ s −1 , the reservoir's recovery degree experiences an increase of 0.091. Simultaneously, the average temperature and average stress of the reservoir increase by 29.66 K and 1.9464 × 10 9 N · m −2 , respectively. At a displacement time of 0.3 s and with the contact angle decreasing from 2π/3 to π/3, the reservoir's recovery degree increases by 0.44537, and the average temperature and average stress of the reservoir increase by 2.87 K and 1.86 × 10 8 N · m −2 , respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".