The Analytic Hierarchy Process for the Reservoir Evaluation in Chaoyanggou Oilfield
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Bibliographic record
Abstract
Reservoir evaluation is one of important contents in the reservoir study. This paper has adopted cluster analysis method to optimize evaluation parameters of low permeability reservoir and the analytic hierarchy process (AHP) to determine the weight coefficient. Moreover, this paper has made the reservoir comprehensive quantitative evaluation for low permeability reservoir of Chaoyanggou oil field. According to the cumulative probability curve, the evaluation results can be divided into three categories, which conform to the low permeability reservoir characteristics of Chaoyanggou oilfield. The method of reservoir comprehensive quantitative evaluation has solved the problems of single-factor classification evaluation that the evaluation result is not unique and provided favorable basis for low permeability reservoir evaluation of Chaoyanggou oilfield. Key words: Reservoir evaluation; Weight coefficient; Low permeability reservoir; Analytic hierarchy; Cluster analysis
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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 | 0.000 |
| 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