Study on the accessible pore volume coefficient of chemical flooding systems under different permeability, layer combination, and planar phase transition conditions
Bibliographic record
Abstract
Abstract In this paper, first, the micro accessible efficiency of chemical flooding under different permeability reservoir conditions is studied by using nuclear magnetic resonance technology. Second, the macroscopic accessible efficiency of chemical agents under different types of sand body layer combinations was elucidated. Third, the macroscopic accessible pore volume correction coefficient of chemical agents under the phase transition conditions between injection and production wells was analyzed. Results show the following: (a) Permeability cannot be the only indicator for evaluating whether a reservoir is suitable for chemical flooding. (b) When the permeability grade ratio is less than 2.27 (10 −3 μm 2 /10 −3 μm 2 ), the correction factor for macroscopic accessible pore volume of chemical agents is 1.00; when the permeability grade ratio has increased to 3.33, the macroscopic accessible pore volume correction factor for chemical agents in non‐main sheet sand reservoirs is 0.68; and when the permeability grade ratio has increased to 16.67, the correction factor for macroscopic accessible pore volume of off balance sheet reservoir chemicals is 0.04. (c) The type and location of phase transition in sand bodies between injection and production wells have a significant impact on the macroscopic accessible volume coefficient of chemical agents. This part of the research is of great significance for selecting acid types based on reservoir properties.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".