Estimating Effective Fracture Pore Volume From Flowback Data and Evaluating Its Relationship to Design Parameters of Multistage-Fracture Completion
Bibliographic record
Abstract
Summary Flowback data from seven multifractured horizontal tight oil/gas wells in Anadarko Basin show two separate regions during the single-phase water production. Region 1 shows a dropping casing pressure, and Region 2 shows a flattening casing pressure. This paper investigates the flowback behavior of the two regions, and illustrates how flowback data can be interpreted to estimate effective fracture pore volume, and to investigate its relationship to completion-design parameters. We construct diagnostic plots to understand the physics of Regions 1 and 2. Region 1 represents pressure depletion in fractures, and Region 2 represents the hydrocarbon breakthrough into the effective fracture network. The results of our analyses indicate that the duration of Region 1 depends on initial reservoir pressure and hydrocarbon type. We apply a previous flowback model (Abbasi et al. 2012, 2014) on Region 1 to estimate effective fracture pore volume, and also propose a procedure to estimate fracture compressibility by use of diagnostic-fracturing-injection-test (DFIT) data. The results suggest that the estimated effective fracture pore volume is very sensitive to fracture compressibility, and is generally larger than the final load-recovery volume, and less than the total injected-water volume. The results also suggest that most of the effective fractures are unpropped, and host the nonrecovered fracturing water. We investigate the relationship between the estimated effective fracture pore volumes and completion-design parameters, including total injected-water volume, proppant mass, gross perforated interval, and number of clusters, by use of the Pearson correlation-coefficient method. The results show that total injected-water volume, gross perforated interval, and the number of clusters are among the key design parameters for an optimal fracturing treatment. Higher total injected-water volume and closer cluster spacing generally lead to a larger effective fracture pore 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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 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 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".