Estimating Effective Fracture Pore-Volume from Early Single-Phase Flowback Data and Relating It to Fracture Design Parameters
Bibliographic record
Abstract
Abstract Flowback data analysis has been recently considered by the industry to quantify hydraulic fracture parameters such as, effective fracture pore-volume. Tools based on simulation and rate transient analysis have been applied for flowback analysis. However, too many unknown parameters, together with complex production mechanisms lead to uncertain and non-unique results. This paper illustrates how flowback data can be interpreted to estimate effective fracture pore-volume and its relationship to fracture design parameters. This study starts by addressing some limitations of the previous flowback models. In particular, the effects of water expansion and fracture closure are included in the proposed model. The resulting linear relationship is applied to flowback data from eight tight oil and gas wells in Anadarko Basin to estimate effective fracture pore-volume. The estimated effective fracture pore-volumes are compared with fracture design parameters such as total injected volume of water, soaking time, gross perforated interval, and proppant concentration. The results indicate that fracture closure is the main mechanism for single-phase water flowback. Therefore, effective fracture pore-volume largely depends on fracture compressibility. The results also show that soaking time, gross perforated index, and proppant concentration are among the key design parameters for an optimum fracturing treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".