Experimental Study on the Effect of Injection Parameters on Proppant Transport in Rough Vertical Hydraulic Fractures
Bibliographic record
Abstract
Abstract The understanding of proppant flow through fractures is critical in evaluating the hydraulic fracturing performance. As a continuation of our experimental efforts devoted to understanding how proppant flows in rough vertical fractures, in this paper, we examine the effect of injection parameters on the proppant transport in rough vertical fractures. The effects of polymer concentration, injection rate, proppant concentration, and type of proppant were investigated in detail. Experimental results show that a sufficiently high polymer concentration is needed to enable effective proppant flow in rough fractures. In general, the relative coverage of proppants increased dramatically as the polymer concentration increased, implying that the higher viscosity of fracturing fluid could enhance the slurry's ability to place more proppant vertically into the fracture and help to maintain a better conductivity after fracturing treatment. A sufficiently high injection rate of the slurry is also needed to enable effective proppant flow in rough fractures. At certain low injection rate, the proppants carried by a low polymer solution might not exhibit a tree-like settling pattern, diminishing the effect of roughness effect on the proppant transport. This means that even in rough fractures, the tree-like settling pattern of the proppants did not necessarily occur for sure; the injection rate should be properly selected to enable such phenomenon. With other condition being kept constant, a higher proppant loading led to a higher final relative coverage of the proppants in the rough fractures. But if the injection rate used for delivering the proppants is not sufficiently high, we may encounter injectivity issues; in our lab experiments, this caused the choking of the pump. The heavier proppant (ceramic proppants) in the rough fracture models tended to suppress the tree-like settling pattern that was experienced by the lighter proppant (silica sands). This is attributed to the larger density of the ceramic proppants, leading to a larger settling velocity. In order to maximize the spreading of a given proppant over a rough fracture model, we should determine the proper values of all the essential injection parameters (including polymer solution, injection rate, proppant concentration) by striking a good balance among them. The conclusions obtained in this study shed light on how to optimize slurry injection parameters to achieve an optimal proppant-filling ratio during hydraulic fracturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 teacher head, not a consensus.
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