Effect of Fracture Roughness, Shear Displacement, Fluid Type, and Proppant on the Conductivity of a Single Fracture: A Visual and Quantitative Analysis
Bibliographic record
Abstract
Summary Proppants are one of the essential parameters in fracturing design. They not only provide fracture conductivity but also prevent “healing” of fractures. Hence, the quantification of proppant-transport characteristics is highly critical in a sustainable production from hydraulically fractured wells. Previous attempts in this regard were limited to smooth (parallel) fracture surfaces to a great extent, but the roughness of fractures may control the conductivity of hydraulic fractures in the presence of proppants. This paper focuses on experimental measurements to visually and quantitatively investigate the hydraulic characteristics of rough fractures in the presence of proppants. Transparent models of the fractures of different origin rocks (granite, marble, and limestone) were prepared. Water and polymeric solutions representing typical rheological properties of hydraulic-fracturing fluids were injected through the models (joint and sheared fractures) with and without propping agents. The conductivity changes caused by proppant distribution caused by the roughness of fracture surfaces were quantified and correlated to different fractal characteristics of surface roughness. Qualitative and quantitative analyses were supported by images collected through the experiments. Proppant behaviors in joint- and shear-type fractures were observed to be different. In both cases, fracture-closure areas existed, which controlled the proppant transportation and fracture conductivity. The qualitative and quantitative data provided on the degree of conductivity change in a single fracture (in the presence and absence of propping agents) are expected to be useful in accurate performance estimation of oil/gas production from fractured systems.
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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) | 0.000 | 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".