Evaluating Casing Damage Basing on Fuzzy Comprehensive Evaluation and Grey Relational Grade Analysis
Bibliographic record
Abstract
Casing damage is one of the main factors influencing oil production, and determined the main factors which lead to casing damage is the premise to develop effective prevention and control measures of casing damage. The relationship between various factors of casing damage is complicated, and it is difficult to determine the main factors influenced the casing damage applying for conventional theoretical analysis and quantitative calculation. In this paper, the main factors influenced casing damage is evaluated by the method of combination fuzzy comprehensive evaluation and grey relational grade analysis. Firstly, this article analyzed factors causing casing damage, and then evaluated 22 wells of Daqing oilfield which is located in the west block of the Southern District fault. Comparing the evaluation and the actual results, the accuracy rate of this model is 86.3%, and showing that the evaluation results are accurate and reliable. Key words: Casing damage; Fuzzy comprehensive evaluation; Grey relational grade; Effect evaluation
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".