Numerical Simulation Study on In‐Depth Profile Control of Core–Shell Coagulation System Considering the Time‐Variation of Permeability
Bibliographic record
Abstract
The coring data in high water‐cut oilfields indicates that the reservoir permeability will change continuously with water flooding, while the existing reservoir numerical simulation software cannot consider the time‐varying phenomenon of permeability. With the enhancement of reservoir heterogeneity, the near‐wellbore profile control fails to stabilize the oil production and control the water cut. The in‐depth profile control has been widely used in oilfields as a new technology, and the types of profile control agents are diverse, with a complex mechanism that cannot be effectively described by the existing conventional numerical simulation software. Considering these two phenomena comprehensively, a new three‐dimensional, three‐phase, six‐component mathematical model that can take into account the time‐varying phenomenon of reservoir permeability is proposed for a new kind of in‐depth profile control system, namely, the core–shell coagulation system, and an integrated numerical simulation software is developed. The mechanism of the in‐depth profile control system can be perfectly demonstrated in the simulator with time‐variation of permeability. The results of sensitivity analysis show that the effect is influenced by three factors: the mix slug injecting concentration, the coagulant aid slug volume, and the concentration of the suspension dispersing agent.
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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 | 0.001 | 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) | 0.001 | 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".