The Application of Fuzzy Clustering Method in the Division of Reservoir Flow Unit
Bibliographic record
Abstract
The reservoir of Beierxi-Puyizu in Sabei developed area comes into the special high water-cut stage. The distribution of residual oil at the later stage of polymer flooding is disperse quietly. The contradiction in layer or among layers is complex, the range of measure reduces gradually and routine measure takes effect poorly. According to the parameters such as permeability and formation capacity, the six sedimentary units of the block are divided into four flow units, the result will provides a more credible geological reference for the identification of single runway in complex channel sand and the adjustment of measures at the later stage of polymer flooding. The microfacies of the adjacent flow units are different, they are generally flow unit of margin facies belt. At the same time, the saturation of residual oil at the juncture of different microfacies belt is high. There is little residual oil exists in the channel sand flow unit of good type and more exists in very good type, it is the target of residual oil tapping at the middle and later stage of polymer flooding. The residual saturation of the flow unit in intermediate belt is high, but there are so many difficulties in tapping the residual oil.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".