Visual Analysis on the Effects of Fracture-Surface Characteristics and Rock Type on Proppant Transport in Vertical Fractures
Bibliographic record
Abstract
Abstract The fracture-surface characteristics (such as roughness and fractal dimensions) may greatly affect the proppant transport during hydraulic fracturing operation. Few researches have focused on investigating the proppant transport in vertical fracture with actual surface characteristics. As a continuation of our previous study (Huang et al. 2017), we qualitatively investigatethe migration of proppants in rough and vertical fractures by considering the effects of surface characteristics and rock type on the instantaneous transport and areal spreading of proppant in the fractures. We fractured different types of tight rocks (including limestone, marble, tight sandstone, and granite) with Brazilian test and molded them to manufacture 20×20cm transparent replicas with an aperture of 1 mm. We characterized the surface characteristics of these rock samples with different fractal dimensions. Subsequently, dyed fracturing fluid with or without proppant loading was injected into the rough vertical fracture. In each test, we monitored the inlet pressure continuously while the proppants were being transported in the fracture. The process was videotaped to monitor the proppant distribution in the rough fracture. Different from our previous study (Huang et al. 2017), a higher injection rate is used in this present study. The experimental results obtained in this study further consolidate the many findings reported in our recent study (Huang et al. 2017): in rough and narrow fracture, the surface roughness plays a pivotal role in affecting how proppants settle in the fracture as well as where the proppants settle in the fracture. Roughness of the vertical fractures tends to significantly enhance the vertical placement of proppants in the fracture, leading to a much higher proppant-filling ratio in a rough fracture than in a smooth fracture. Interestingly, in addition to the bridging effect observed in Huang et al. (2017), a previously formed proppants cluster can be broken up under a higher-rate slurry flow. The bridging of proppants and its subsequent breaking up can recursively occur during the high-rate slurry flow, resulting in fluctuations in the proppant filling ratios as well as fluctuations in the pressure profiles recorded in the inlet of the fracture model. The roughness of fracture models not only affects how much area of the fracture is being occupied by the proppants in the fracture, but also affects how tightly the proppants are filling up the fracture. Different types of rock have different surface characteristics, leading to the observed differences with regard to how the proppants migrate, settle down and fill up the fractures. No definite correlation could be established between any of the fractal numbers and the relative coverage of proppants in the fracture. More experiments, however, need to be conducted to reach more concrete conclusions in this regard.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".