The Analysis of Dynamic Data of Multi-Fractured Horizontal Well Preliminary Application Research
Bibliographic record
Abstract
Multi-fractured horizontal well’s model is complicated, which have to consider lots of parameters and bring other difficulties for the analysis of dynamic data. This paper intends to identify the difference from a theoretical perspective first and consider the nonlinearity of numerical horizontal method can explain or evaluate for these wells. Then this paper based on an actual multi-fractured horizontal well of gas and utilize the new model to analysis of dynamic data of multi-fractured horizontal well with different analysis method, through contrast with each result concluded that the nonlinearity of numerical horizontal model is the most appropriate for the analysis of dynamic data of multi-fractured horizontal well. The nonlinearity of numerical horizontal method has considered interferences between fractures and nonlinearity PVT and other factors, which are advanced than other methods and this method is the most appropriate for the analysis of dynamic data of multi-fractured horizontal well up to now. Key words: Multi-fractured; Horizontal well; Analysis of dynamic data
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".