The Study and Application of Gray-Orthogonal Wavelet Network Forecasting Mode on Cementing Quality
Why this work is in the frame
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Bibliographic record
Abstract
The interrelated characteristics and the complexity of cementation quality affecting factors are considered, a new method of forecasting cementing quality based on Gray-Orthogonal wavelet network is set up. The method regards some factors affecting the cementing quality as the input parameters of the model, and regards quantifying cementation quality as the output parameter of the model. Evaluating and forecasting the cementing quality of Bei1 block of 20 wells in Daqing Oilfield. The maximum fractional error between forecasting results and actual results is 2.66%. The associated problem of impacting factors is solved by the new forecasting method，and the prediction accuracy of cementing quality is improved. A reliable theoretical basis of cementing operation parameters design and construction program adjustment for new wells in oil field is provided. Key words : Cement job quality; Gray system; Orthogonal wavelet neural network
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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