Average Fracture Compressibility from Flowback Data
Bibliographic record
Abstract
Summary We propose a novel method for estimating average fracture compressibility cf¯ during flowback process and apply it to flowback data from 10 multifractured horizontal wells completed in Woodford (WF) and Meramec (MM) formations. We conduct complementary diagnostic flow-regime analyses and calculate cf¯ by combining a flowing-material-balance (FMB) equation with pressure-normalized-rate (PNR)-decline analysis. Flowback data of these wells show up to 2 weeks of single-phase water production followed by hydrocarbon breakthrough. Plots of water-rate-normalized pressure and its derivative show pronounced unit slopes, suggesting boundary-dominated flow (BDF) of water in fractures during single-phase flow. Water PNR decline curves follow a harmonic trend during single-phase- and multiphase-flow periods. Ultimate water production from the forecasted harmonic trend gives an estimate of initial fracture volume. The cf¯ estimates for these wells are verified by comparing them with the ones from the Aguilera (1999) type curves for natural fractures and experimental data. The results show that our cf¯ estimates (4 to 22×10−5 psi−1) are close to the lower limit of the values estimated by previous studies, which can be explained by the presence of proppants in hydraulic fractures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".