Quantitative and Visual Analysis of Proppant Transport in Rough Fractures and Aperture Stability
Bibliographic record
Abstract
Abstract The consensus reached in the literature is that the roughness of fractures plays a crucial role on proppant transport affecting the aperture sustainability of hydraulic fractures. In this paper, an experimental scheme to visually and quantitatively investigate the hydraulic characteristics of rough fractures in the presence of proppants was presented. Rock samples of different kinds (i.e., granite, marble, and limestone) were fractured under the Brazilian test and molded to manufacture 20x20 cm transparent replicas. Propping agents were injected in a similar fashion and were introduced into the well with fracking fluid at a constant rate. Two types of fracture models were used: (1) perfectly mating (joint) and (2) sheared fractures in polymeric solutions. During the experiments, the inlet pressure was continuously monitored to quantify the permeability changes due to proppant distribution caused by the roughness of fracture surfaces. Meanwhile, corresponding images were collected to trace the transport of proppants and their behavior was correlated to the measured permeability change. For a better visualization of proppants, the injected fluid was dyed with a fluorescent material. In both joint and shear type fractures, existing closure areas controlled the proppant movement and permeability change significantly. The fracture roughness controlled by the lithological properties of the rocks was a critical factor affecting the permeability and proppant transport. After quantifying the roughness characteristics through the variogram fractal dimension, relationship between fracture permeability in the presence of proppant and rock types were presented. Also provided was a semi-quantitative analysis of the stability (or settlement) of proppants during injection with respect to the roughness type (and lithology). The quantitative and visual data collected for a wide range of rock types with original roughness characteristics are expected to be useful in fracking design and selection of proper proppants for different reservoirs.
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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 | 0.001 | 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".