Study on Failure Length of Cementing Interface in Horizontal Wells During Fracturing
Bibliographic record
Abstract
The cementing interface of oil and gas wells is often the weak link between oil and gas turbulence. Due to the low cementation strength at the fracturing interface, the two interfaces have been crushed to form turbulence channels before the target layer is opened during fracturing. If the closure is not good, there will be inter-layer channeling. Therefore, the pressure bearing capacity of the fracturing interface is an important indicator for designing the fracturing construction parameters. The pressure capacity of the two interfaces during the fracturing process is the key to evaluating the success of the fracturing construction. This paper establishes the calculation model for the stress distribution of horizontal wells in horizontal wells under the effect of non-uniform stress. At the same time, the influence of the pressure change in the wellbore during the fracturing process on the stress distribution in the borehole wall was analyzed. The calculation model of the interfacial stress distribution in the horizontal well during the fracturing process was established, and the debonding pressure and debonding length of the two interfaces under different cementing strengths were calculated. After the establishment of the horizontal well fracturing two interface crack propagation mechanics model, calculate the pressure required for cracks along the two interfaces to expand at different failure lengths.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".