Effect of Fracture Surface Roughness and Shear Displacement on Permeability and Proppant Transportation in a Single Fracture
Bibliographic record
Abstract
Abstract Proppants are one of the essential parameters in fracking design. They not only provide fracture permeability but do prevent “healing” of fractures. Hence, the quantification of proppant transport characteristics is highly critical in a sustainable production from hydraulically fractured wells. Previous attempts were limited to smooth (parallel) fracture surfaces, to a great extent. The consensus reached in the literature, however, is that the roughness of fractures may play a crucial role on proppant transport affecting the permeability 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 is presented. Seven 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 at a constant rate into perfectly mating (joint) and sheared fractures in water and polymeric solutions representing typical rheological properties of hydraulic fracturing fluids. During these 2-D experiments, the inlet pressure was continuously monitored to quantify the permeability changes due to proppant distribution caused by the roughness of fracture surfaces. Simultaneously, 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. The proppant behavior in joint and shear type fractures were different. In both cases, fracture closure areas existed, which controlled the proppant movement and permeability change significantly. The injection rate, proppant size, and fracture roughness controlled by lithological properties of the rocks were the other critical factors affecting the permeability and proppant transport. After quantifying the roughness characteristics through different fractal methods (e.g., variogram analysis, power spectral density, etc.), correlations between fracture permeability in the presence of proppant and rock types were presented. 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. Key words: Proppant transport, fracture roughness, joint and shear fractures, fracture permeability, fractal fracture surfaces. Introduction The main goal of hydraulic fracturing is to provide permeable flow path for hydrocarbons in tight formations. The stability of this permeable flow path can be achieved by propping agents that are injected with treated water. Design of fracturing fluid treatment together with selecting proper proppant type critically impacts the hydrocarbon recovery from the formation (Coulter et. al.2004; Terracina et al. 2010; Kassis et al. 2010; Ribeiro and Sharma 2012, 2013). The mechanism of proppant transport in rough-walled fractures and its effect on permeability should be understood clearly in the assessment of recovery performance, as well. Proppant transport depends on the distribution of asperities, surface roughness, and contact area, which are all controlled by lithological properties of the rocks (Fredd et al. 2000). In addition, rough surface coupled with shear displacement causes closures of the fracture at some points and this eventually affects the proppant transport (van Dam and de Pater 1999).
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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 | 0.000 | 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) | 0.001 | 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".