Flowback Analysis for Fracture Characterization
Bibliographic record
Abstract
Abstract Tight reservoirs stimulated by multistage hydraulic fracturing are commonly characterized by analyzing the hydrocarbon production data. However, analyzing the available hydrocarbon production data mainly determines the fracture-matrix interface. This analysis is not enough for a full characterization of the induced hydraulic fractures. Before putting the well on flowback, the induced fractures are occupied by the compressed fracturing fluid. Therefore, analyzing the produced fracturing fluid should in principle be able to characterize the induced fractures, and complement the production data analysis. We develop a rate transient model for describing the fracturing fluid flowback. We also make various diagnostic plots for understanding the flowback behavior of three fractured horizontal wells. The diagnostic plots indicate three separate flowback regions. In the first region, water production dominates while in the third region hydrocarbon production dominates. In the second region, water production drops and hydrocarbon production ramps up. In general, we observe a linear relationship between rate normalized pressure (RNP) and material balance time (MBT) for the three regions. However, the proposed model can only describe the response of the first region. We successfully determine the hydraulic fracture permeability by history matching the early time flowback data. We conclude that the flowback analysis can complement the production data analysis for a comprehensive fracture characterization. The presented study encourages the industry to start careful measurement of the rate and pressure data immediately after putting the well on hydraulic fracture flowback.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".