Advances in Flowback Data Analysis for Estimating Average Fracture Compressibility: North American Case Studies
Bibliographic record
Abstract
Abstract In this study, we propose a new method for estimating average fracture compressibility (cf¯) during flowback process, and apply it on flowback data from thirty multi-fractured horizontal wells completed in Eagle Ford, Horn River, Montney and Woodford formations. We conduct complementary diagnostic flow regime analyses and calculate cf¯ by combining a flowing material balance equation with rate-decline analysis. We observe two production signatures during flowback: (1) single-phase water production followed by hydrocarbon breakthrough and (2) immediate production of hydrocarbon with water. Water rate-normalized-pressure plots show pronounced unit slopes, suggesting pseudo-steady state flow. Water decline curves follow a harmonic trend during multiphase flow; from which we forecasted ultimate water production as an estimate of initial fracture volume. The cf¯ estimates are within the range of 7 – 200 × 10-6 psi-1, and are generally lower than the values previously estimated using Aguilera’s type curves and DFIT data. Also, cf¯ estimates for deeper dry gas wells are relatively higher than those for shallower oil wells.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".