A semi‐analytical model for capturing dynamic behavior of hydraulic fractures during flowback period in tight oil reservoir
Bibliographic record
Abstract
Abstract Hydraulic fracturing has been successfully employed for unconventional oil and gas recovery for decades. During flowback, the closure of the fracture may exhibit with the pressure drop of fracturing fluid dewatering. However, fracture closure always is ignored or treated as stress‐dependent fracture properties in previous flowback models. This paper presented a dynamic fracture model, which can comprehensively capture the dynamic behavior of hydraulic fractures during the flowback. A nonlinear relationship between fracture aperture and contact stress acting on the fracture surfaces is adopted to simulate fracture closure. The fracture aperture calculated by the displacement discontinuity method (DDM) is used to characterize the fracture pore volume and fracture conductivity, which will be dynamically updated in the flow model. Then, the pressure and saturation of each phase, along with the displacement on the fracture surface, are calculated by solving flow equations and geomechanics equations with iterative coupling approach. The new semi‐analytical model is validated by comparing it with a fully coupled stress‐porosity pressure numerical simulation model setup by ABAQUS® and CMG. Then, the dynamic behaviors of hydraulic fractures are investigated in detail by several cases. Results show that fracture closure is an important reason for the decline in production during the flowback and early production. And it is more important to enhance the properties of the stimulated reservoir volume (SRV) than to only create a fracture with high conductivity. Lastly, the key parameters (the fracture effective length and fracture conductivity under variable contact stress) can be interpreted by history‐matching the field flowback data.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".