FRACTURING FLUID LEAKOFF BEHAVIOR IN NATURAL FRACTURES: EFFECTS OF FLUID RHEOLOGY, NATURAL FRACTURE DEFORMABILITY, AND HYDRAULIC FRACTURE PROPAGATION
Bibliographic record
Abstract
This study developed a coupled model for the fracturing fluid loss into natural fractures during a hydraulic fracturing treatment. The model considers a non-Newtonian fracturing fluid, the influence of hydraulic fracture, and the fluid exchange between hydraulic and natural fractures. A semianalytical solution is proposed to solve the coupled model, which demonstrates reliable results through validation study. Parametric studies indicate that increasing the fracturing fluid viscosity can effectively reduce its leakoff at a low viscosity level. While at a high level, further enlarging fluid viscosity may not lead to better control of leakoff. In addition, larger deformability of reservoir rock leads to a faster pressure diffusion and a greater fluid leakoff. Also, the pressure profile inside a fracture takes a concave shape when the deformation of a natural fracture is considered. Moreover, it is found that when a large number of natural fractures open at the same time, fluid leakoff can cause an immediate reduction in the hydraulic fracture width and even screen-out. Therefore, to reduce the operation risks of hydraulic fracturing, the pump rate and fluid rheology must be carefully designed based on the properties of natural fractures in reservoirs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".