WITHDRAWN: The imbibition mechanism and the calculation method of maximum imbibition length during the hydraulic fracturing
Post-publication record
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Bibliographic record
Abstract
It was acknowledged that fluid imbibed into matrix and flooded oil during the hydraulic fracturing; however, the mechanism of fluid imbibed into matrix and flooded oil still kept unclear. Meanwhile, the maximum imbibition length calculation method was still scarce. In the paper, we firstly analyzed the imbibition mechanism during the hydraulic fracturing, and then built an imbibition length calculation method by the mercury intrusion experiment, seepage theory, and numerical calculation method. By comparing with the experimental results and model calculation results, our proposed method was verified. Finally, the influences of the maximum imbibition length were presented. From the work, it was concluded that the imbibition during the hydraulic fracturing was the counter-current imbibition with the surround pressure. The effect of the permeability on threshold pressure gradients was greater than that on the capillary pressure, which caused that the maximum imbibition length increased with an increased permeability (from 0.01 to 0.2×10-3μm2), but the time of achieving maximum imbibition lengths decreased exponentially. When the reservoir permeability was 0.1×10-3 μm2, the contact angle was 60°, and the interface tension was 50 mN/m, the maximum imbibition length was 1.8 m, and the time of achieving maximum imbibition length was 70 days. This study provided a method for evaluating the extent of imbibition draw.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".