The Application of Fuzzy Comprehensive Evaluation in Deepwater Gas Well Testing String Risk Assessment
Bibliographic record
Abstract
Offshore Testing is a world-wide difficult problem with high danger and high risk. Many technical problems in the field of national offshore oil production are still pending. The safety of testing string is affected by multiple complex factors, and it is a complicated nonlinear problem with marked deformation and indeterminacy. The traditional risk assessment methods no longer meet the need for risk assessment of testing string. This paper adopts fuzzy comprehensive evaluation, which is based on the AHP (analytic hierarchy process) to assess the security of strings of deepwater gas well. First of all, it makes model, analyses the factors of the risk, divides the hierarchy and adopts AHP (analytic hierarchy process) to determine the weight of each factor. Secondly it seeks the experts’ reviews to establish the aggregation of comments, researches the effect of each assessment factors, makes fuzzy assessment of each factor, and makes fuzzy information of many describing different aspects of the object which has different dimensions quantification. Lastly, it makes fuzzy comprehensive evaluation in order to make sure the risk assessment level of testing string and achieve quantitative analysis of the risk factors that effect testing string and assess the safety of testing string scientifically.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".