Dynamics of proppant particle settling within low Reynolds numbers: Roles of particle shape, surface wettability, wall factor, and fluid elasticity
Bibliographic record
Abstract
Characterizing proppant settling in fracturing fluids is essential for optimizing hydraulic fracturing operations. However, the dynamics of proppant settling in viscoelastic fluids within low Reynold numbers remain unclear. In this work, we investigate the settling behavior of real proppants in a narrow space filled with viscoelastic partially hydrolyzed polyacrylamide (HPAM) solutions to quantify the effects of proppant shape, surface wettability, fracture walls, and fluid elasticity on settling dynamics. Experimental results indicate wall factors lower than studies in literature and negligible influence of particle surface wettability on settling despite different contact angles between resin-coated and non-coated proppants. Fluid elasticity reduces the drag on proppants exponentially, which supports an observation that making thicker HPAM solutions does not always lead to slower proppant settling. New correlations of drag coefficient and terminal settling velocities are developed to quantify the effects of wall retardation and fluid elasticity on particle settling in viscoelastic fluids. • Wall factors are inferred from measured velocities against Renaud correlation. • Fluid elasticity reduces the drag exponentially regarding relaxation time variation. • Proppant shape and surface wettability show negligible impact on proppant settling. • New correlations of drag coefficient and terminal velocity are developed.
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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 | 0.000 |
| 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) | 0.001 | 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".