Steam Cavity Expansion Model for Steam Flooding in Deep Heavy Oil Reservoirs
Bibliographic record
Abstract
Steam flooding is crucial for the development of heavy oil reservoirs, and the development of the steam cavity significantly determines the efficiency of steam flooding. Previous studies have elucidated the concept of steam overburden and pseudomobility ratio; however, the thermal energy loss in deep heavy oil reservoirs during steam injection needs further investigation. Therefore, in this study, the vapour–liquid interface theory and mathematical integration were used to establish a steam cavity expansion model. The wellbore heat loss rate coefficient, steam overlay, and pseudomobility ratio were used to accurately describe the development of the steam cavity in deep heavy oil reservoirs. The proposed model was experimentally validated, and it was observed that the model could accurately reflect the actual mine conditions. In addition, the pressure gradient distribution of the steam belt and the heat dissipation areas of the top and bottom layers of the steam cavity were evaluated. The results showed that the influence of the wellbore heat loss rate coefficient on the pressure gradient of the oil layer was primarily in the range of 5–20 m away from the steam injection well. Furthermore, it was observed that the pseudomobility ratio is inversely proportional to the development of the steam cavity. As the wellbore heat loss rate coefficient increased, the wellbore heat loss increased. The larger the area ratio, the more pronounced the steam overlay phenomenon, and the large area ratio does not meet the development requirements of the steam chamber. The research closely combines theory with production, and the results of this study can help actual mines by providing theoretical support for the development of deep heavy oil reservoirs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".