Evaluating Fracture Volume Loss During Flowback and Its Relationship to Choke Size: Fastback vs. Slowback
Bibliographic record
Abstract
Summary In this study we estimated the initial effective fracture pore volume (Vfi) and fracture volume loss (dVef) for 21 wells completed in the Montney and Eagle Ford formations. We also evaluated the relationship between dVef and choke size. First, we applied rate-decline analysis to water-flowback data of candidate wells to estimate the ultimate water recovery volume, approximated as Vfi. Second, we estimated dVef using a fracture compressibility relationship to evaluate the fracture volume loss of the Eagle Ford wells. Third, we investigated the effect of choke size on dVef for the Eagle Ford fastback and slowback wells. Semilog plots of flowback water rate vs. cumulative water volume show straight-line trends, representing a harmonic decline. The estimated Vfi accounts for approximately 84 and 26% of the total injected water volume (TIV) of the Montney and Eagle Ford wells, respectively. The results show that approximately 10% of the fracture volume is lost during flowback. This loss in fracture volume predominantly happens during the early flowback and becomes minimal during the late flowback period. The results show a relatively higher dVef for fastback (a flowback process with a relatively large choke size) wells compared with that for slowback (a flowback process with a relatively small choke size) wells. In this study we proposed a method to estimate the initial fracture volume and investigated the loss in fracture volume during the flowback process. Analyses of the field data led to an improved understanding of the factors that control water flowback and the effective fracture volume.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".