Evaluating fracture volume loss during production process by comparative analysis of initial and second flowback data
Bibliographic record
Abstract
Abstract The fracture volume is gradually changed with the depletion of fracture pressure during the production process. However, there are few flowback models available so far that can estimate the fracture volume loss using pressure transient and rate transient data. The initial flowback involves producing back the fracturing fluid after hydraulic fracturing, while the second flowback involves producing back the preloading fluid injected into the parent wells before fracturing of child wells. The main objective of this research is to compare the initial and second flowback data to capture the changes in fracture volume after production and preload processes. Such a comparison is useful for evaluating well performance and optimizing fracturing operations. We construct rate-normalized pressure (RNP) versus material balance time (MBT) diagnostic plots using both initial and second flowback data (FBi and FBs, respectively) of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin. In general, the slope of RNP plot during the FBs period is higher than that during the FBi period, indicating a potential loss of fracture volume from the FBi to the FBs period. We estimate the changes in effective fracture volume (V ef) by analyzing the changes in the RNP slope and total compressibility between these two flowback periods. V ef during FBs is in general 3%–45% lower than that during FBi. We also compare the drive mechanisms for the two flowback periods by calculating the compaction-drive index (CDI), hydrocarbon-drive index (HDI), and water-drive index (WDI). The dominant drive mechanism during both flowback periods is CDI, but its contribution is reduced by 16% in the FBs period. This drop is generally compensated by a relatively higher HDI during this period. The loss of effective fracture volume might be attributed to the pressure depletion in fractures, which occurs during the production period and can extend 800 days.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".