Determining a Minimum Polymer Concentration to Optimize Dynamic Proppant Suspension and Transport at a Particular Shear Rate and Proppant Concentration
Bibliographic record
Abstract
Abstract For hydraulic fracturing treatments, propped area and fracture efficiency are dependent on the proficiency of proppant transport into the fracture aperture. Resultant fracture conductivity is dependent on how well proppant can be distributed throughout the fracture. Typically, two mechanisms are present that can assist with proppant transport: inertial effects and rheological effects. Within the wellbore, at high Reynolds numbers, inertial effects are the dominant mechanism for proppant transport. Within the reservoir, the rheological properties of the fluid govern proppant diffusion, along with fracture geometry and fluid leak off. Building on this foundation, our research introduces a new method for quantifying DPS in hydraulic fracturing, enhancing traditional techniques with a modified rotational coaxial cylinder viscometer and image processing technology to precisely measure proppant settling rates under specific shear conditions. The proposed methodology provides a novel empirical method for quantifying DPS for bulk proppant samples at controlled shear rates. Utilizing high-speed cameras with high-resolution image processing techniques, we delve into proppant transport phenomena, focusing on the interplay between fluid dynamics, proppant characteristics, and chemical properties. This approach aims to optimize proppant transport efficiency, ensuring friction reducers (FR's) are selected and designed with a comprehensive understanding of their performance. By highlighting the significance of inertial and rheological effects across various shear rates and the impact of fracture geometry on proppant transport, our work seeks to enhance proppant efficiency and resulting conductivity. Ultimately, our integrated methodology offers valuable insights into proppant behavior under varying conditions and the pivotal role of determining a minimum polymer concentration to measure proppant suspension and efficiency. The methodology allows for the inclusion of high concentrations of proppant as an alternative to single particles, static settling tests that are often modeled utilizing Stoke's law for settling velocity. The rotational speed of the rheometer's external concentric cylinder can be adjusted to emulate reservoir shear conditions. Settling rates are continuously acquired, and the data can be analyzed temporally to study the shear effects on the fluid. The cross-sectional area of the fluid column that contains a certain threshold of proppant is calculated.
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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.
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".