A model and type curves for analyzing shut-in pressure based on multistage closure of fracture networks in volume fracturing
Bibliographic record
Abstract
To improve post-fracturing evaluation in unconventional gas reservoirs, shut-in pressure decline analysis is employed as a diagnostic method valued for its cost-effectiveness and data reliability. However, conventional interpretation models, which are primarily based on the assumption of simple, bi-wing fractures, often fail to account for the complexities introduced by natural fractures. This limitation hinders the accurate interpretation of the complex fracture networks typically created in unconventional reservoirs. To bridge this gap, this study develops a novel multi-stage shut-in pressure analysis model by integrating seepage mechanics with material balance theory. The model explicitly incorporates the multi-stage closure behavior of natural fractures as pressure declines. A suite of corresponding G-function diagnostic plots was also established to facilitate application. A comprehensive sensitivity analysis reveals the distinct influence of key parameters, including the leak-off coefficient, fracture compliance, and fracture area, on the diagnostic plots. Validation against field data from a tight gas reservoir demonstrates that the proposed model not only matches the observed pressure decline with high accuracy but also effectively quantifies critical post-fracturing parameters. Consequently, this work provides a robust and practical tool for the quantitative evaluation of stimulation effectiveness in unconventional gas reservoirs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".