Research on the construction of a working fluid system model for ultra-high temperature dense pressurized leakage prevention and plugging combined with multivariate nonlinear regression and machine learning optimization
Bibliographic record
Abstract
At present, drilling luid leakage in oil and gas drilling engineering in complex formations is a worldwide technical problem.The study explains the mechanism of dense pressure-bearing plugging at the bottom of the fracture, explores the in luencing factors of the pressure-bearing capacity of the leakage prevention and plugging working luid, and establishes a mathematical model by using multivariate nonlinear regression analysis.Based on the machine learning technology, the support vector machine algorithm is selected as the prediction method of the particle size of the working luid for leakage prevention and plugging, and the system model of the ultra-high-temperature dense pressurized leakage prevention and plugging working luid is constructed.It is found that the established multivariate nonlinear regression analysis has good it and accuracy, and the average relative error is only 2.9%, and the seam width (-0.694) and formation pressure (0.502) have the greatest in luence on the pressure-bearing capacity of the working luid for leakage prevention and plugging.The prediction accuracy of the support vector machine model for the working luid particle size was 95.36%, and the prediction F1 values on multiple datasets were all greater than 0.9, showing excellent prediction results.The constructed mathematical model can be used to guide the ield operation, which is conducive to the long-term stable plugging and scienti ic leakage prevention of issure-based leakage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".