Tripping Friction Model for Multi-Stage Fracturing and Completion String in Horizontal Well
Bibliographic record
Abstract
The structure of multi-stage fracturing completion string in horizontal well is complicated. The downhole tools such as packers and sliding sleeves whose dimensions are very close to the size of the borehole, and the completion string has strong stiffness as well. Thus, it leads to larger frictional restriction when running string. Based on the above reasons, it is essential to calculate the tripping capacity before the strings running into the well in case of sticking off. However, calculation errors of conventional string tripping models are relatively larger. This paper took the structure of multi-stage fracturing completion string into consideration, divided completion string by contact points between string and borehole to establish the stress and bending model of the string between two contact points, and established the tripping friction and hookload model for multi-stage fracturing completion string. An applied example of multi-stage fracturing horizontal well in Hong 90-1 block of Jilin Oil Field shows that the created model in the paper is more accurate. The accuracy of hookload while the string running in form curved section to bottom is 95.80%. The established model is more accurate and reliable. It can be used to estimate the tripping ability of the multi-stage fracturing completion string. Key words : Multistage fracturing; Tripping; Tripping friction; Mechanical model
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".