A New Productivity Prediction Model for Multi-fractured Horizontal Wells in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract The productivity of Multi-fractured horizontal wells (MFHWs) in the tight oil reservoirs is the key index to evaluate the effect of volume fracturing. In this study, to find a solution for this problem, firstly, the big data affecting the productivity of MFHWs were collected, and then a multi-level evaluation system was built by using the analytic hierarchy process(AHP). Secondly, the gray theory was used to calculate the correlation coefficients between different parameters and production capacity to screen out the key parameters affecting the production capacity. Finally, a new horizontal well productivity prediction model by coupling key geological and engineering parameters was established, to calculate the similarity coefficient and realize quantitative prediction of single well productivity. The results show that the length of horizontal section, formation pressure, fracturing fluid volume, number of stages, net pay thickness and permeability are the key parameters affecting productivity of MFHWs. The new productivity prediction model has been successfully applied to 175 wells in China’s typical tight oil fields, with prediction errors less than 5%. It can be used in productivity prediction of horizontal wells after volume fracturing in similar unconventional tight oil reservoirs, has broad application prospects, and can guide the efficient development of and fracturing scheme selection for tight oil reservoirs effectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".