A Concise Review of Experimental Works on Proppant Transport and Slurry Flow
Bibliographic record
Abstract
Abstract Final proppant distribution inside hydraulic fractures which depends on particle properties, movement and deposition highly impact wellbore productivity and consequently is crucial in modeling and design of hydraulic fracturing. This paper presents a thorough review of laboratory scale tests performed on proppant transport related to hydraulic fracturing treatments and governing physics behind its mechanisms. The interaction between fluid (gas and liquid) and solid particles has been investigated in applied mathematics and physics. In such phenomena, there is always a relative motion between particles and fluids. In this work this relative motion during proppant movement, sedimentation and fluidization in both small- and large-scale lab tests have been assessed in detail. Existing correlations which relate proppant particles settling velocity to concentration of proppant particles, fracture wall and inertia effect in Newtonian and non- Newtonian fluid are presented as well. Lab tests show that various parameters determine the proppant particles distribution inside the fractures. Particle settling velocity, an influential parameter in this regard, is impacted by fracture walls, inertia and the presence of other particles. Inertia changes the relation of drag coefficient and Reynold number. Fracture wall and particles concentration decrease settling velocity as drag force increases. At a certain level, concentration reaches to its limit. Proppant concentration, in addition, increases the suspension viscosity, fracture width and net pressure. However, it deceases the fracture length as more pressure loss occurs along the fracture. As a result, well productivity is highly impacted by the proppant settling and distribution. Many studies have been devoted to identifying different aspects of hydraulic fracturing and proppant transport mechanisms in porous media. This study highlights the key parameters and their effects, existing correlations and physics behind them for better understanding and management of this mechanism.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".