Fuzzy Comprehensive Evaluation in Well Control Risk Assessment Based on AHP: A Case Study
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Bibliographic record
Abstract
To give a quantitative description of well control risk, a multi-layer fuzzy comprehensive evaluation based on AHP (analytic hierarchy process) is used. During the evaluation, risk factors and weight are given by Delphi method and AHP method. A multi-level and multi-factor evaluation system is built including four level-one factors of geologic uncertainty, well control equipments, techniques and crew quality, and fourteen level-two factors. Then a calculation is given with an oilfield in West China. The result shows geologic uncertainty is the primary factor leading to well control risks and the grade of well control risk is “higher risk”. The application result indicates that well control risk assessment by fuzzy comprehensive evaluation is feasible. Key words : Risk assessment; Fuzzy comprehensive evaluation; Analytic hierarchy process; Weight; Risk factor
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it