Impact of Fracture Topology on the Fluid Flow Behavior of Naturally Fractured Reservoirs
Bibliographic record
Abstract
Fluid flow modeling of naturally fractured reservoirs remains a challenge because of the complex nature of fracture systems controlled by various chemical and physical phenomena. A discrete fracture network (DFN) model represents an approach to capturing the relationship of fractures in a fracture system. Topology represents the connectivity aspect of the fracture planes, which have a fundamental role in flow simulation in geomaterials involving fractures and the rock matrix. Therefore, one of the most-used methods to treat fractured reservoirs is the double porosity-double permeability model. This approach requires the shape factor calculation, a key parameter used to determine the effects of coupled fracture-matrix fluid flow on the mass transfer between different domains. This paper presents a numerical investigation that aimed to evaluate the impact of fracture topology on the shape factor and equivalent permeability through hydraulic connectivity (f). This study was based on numerical simulations of flow performed in discrete fracture network (DFN) models embedded in finite element meshes (FEM). Modeled cases represent four hypothetical examples of fractured media and three real scenarios extracted from a Brazilian pre-salt carbonate reservoir model. We have compared the results of the numerical simulations with data obtained using Oda’s analytical model and Oda’s correction approach, considering the hydraulic connectivity f. The simulations showed that the equivalent permeability and the shape factor are strongly influenced by the hydraulic connectivity (f) in synthetic scenarios for X and Y-node topological patterns, which showed the higher value for f (0.81) and more expressive values for upscaled permeability (kx-node = 0.1151 and ky-node = 0.1153) and shape factor (25.6 and 14.5), respectively. We have shown that the analytical methods are not efficient for estimating the equivalent permeability of the fractured medium, including when these methods were corrected using topological aspects.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".