Pore‐scale flow simulation of high‐viscosity crude oil in anisotropic pore channels
Bibliographic record
Abstract
Abstract Anisotropy is one of the key factors affecting reservoirs development. Previous studies on anisotropic porous media reservoirs have predominantly centred on continuous‐scale and regular models, while studies about irregular pore‐scale anisotropic reservoirs are relatively less frequent. This study aims to establish an irregular anisotropic pore scale reservoir model and investigate its flow characteristics. A two‐dimensional irregular anisotropic pore scale reservoir model is established by using the quartet structure generation set (QSGS) method. By computational fluid dynamics (CFD), a numerical simulation analysis is performed to compares the differences in absolute permeability, heavy oil recovery rate, and remaining oil distribution in different directions. The results indicate that the differences of absolute permeability are relatively small within the same (horizontal or vertical) direction. However, the absolute permeability of horizontal direction is approximately twice that of the vertical direction. When the water injection velocities increase from 0.001 to 0.015 m · s −1 , the differences in horizontal direction of heavy oil recovery rates are very small, all less than 0.0933. However, they are relatively large in the vertical direction, all greater than 0.1778. Under the same conditions (except for different combinations of water injection inlet and outlet), the heavy oil recovery rate is positively correlated with the average pore length on the water injection inlet side. The longer the average pore channel length on the inlet side, the higher the heavy oil recovery rate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".