Dynamic Fracture Volume Estimation using Flowback Data Analysis and its Correlation to Completion-Design Parameters
Bibliographic record
Abstract
Abstract Hydraulic fracturing combined with horizontal drilling is the key to unlocking vast unconventional reservoirs. However, understanding the relationship between fracturing/completion-design parameters and the process efficiency remains challenging. The objectives of this paper are 1) to estimate initial fracture volume and its variations during the production by using flowback data and 2) to investigate the existence of correlations between completion-design parameters and induced fracture volume process optimization. We analyze flowback data and completion-design parameters of 16 shale-gas completed in the Eagle Ford Formation. First, we estimate ultimate water recovery and initial fracture volume by using harmonic-decline model, and fracture volume loss during flowback by using a new iterative approach that accounts for fracture-porosity changes with time. Then, we conduct a multivariate analysis to develop empirical correlations of completions-design parameters with initial fracture volume and fracture characteristic-closure rate (FCR). The results show that harmonic-decline model could be used to estimate initial fracture volume with an average absolute percentage error (AAPE) of 7%. The correlations developed between initial fracture volume and completion-design parameters show that the proppant concentration has the most significant effect on fracture volume, followed by gross perforated interval (GPI) and shut-in time, respectively. Total vertical depth (TVD) and fluid injection rate have insignificant effects. The results indicate that increasing choke size during early flowback leads to a relatively-sharp decrease in fracture volume, while changing choke size during late flowback has negligible effects. The proposed correlation between FCR and completion-design parameters demonstrates the significant effect of proppant concentration on fracture closure during flowback, while GPI and TVD have negligible effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".