A Computational Model to Simulate Proppant Transport and Placement in Rough Fractures
Bibliographic record
Abstract
Abstract Hydraulic fracturing creates rough fracture surfaces, instead of smooth ones, in subsurface formations. It is challenging to simulate the complex proppant transport phenomena in rough fractures due to the roughness effect as well as the complex nature of the coupled particle-fluid two-phase flow. This study first establishes realistic rough fracture models using digital scanning images of real fracture surfaces, and then conducts numerical simulations on the proppant transport and placement dynamics occurring on those rough surfaces. The digital scanning images of the artificially created tensile fractures are used to establish the geometry models of the rough fractures. The Computational Fluid Dynamic (CFD) method is adopted to describe the fluid flow, while the Discrete Element Method (DEM) is adopted to describe the particle motion. A resolved CFD-DEM coupling approach is established to simulate the fluid-granular interactions by properly modeling the momentum exchange between fluid flow and particle motion. We obtain the following preliminary simulation results: the proppant transport and settlement characteristics in rough fractures appear to be drastically different from those in smooth fractures, and the roughness feature tends to increase particle-wall and particle-particle contact. We observe an attenuated particle velocity in rough fractures compared to what occurs in smooth fractures. Additionally, the roughness increases the possibility of proppant settling when particles encounter a location with a large roughness height. Through comparison of the proppant transport phenomena in flat and rough fractures, it is observed that there is a great chance for the rough fractures to create tree-like proppant dunes, which would be beneficial for forming a proppant-filled flow channel with a higher and more sustainable conductivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".