Relationship Between the Geometric Parameters of Rock Fractures, the Size of Percolation Clusters and REV

Tóth, Tivadar M.; Vass, István

doi:10.1007/s11004-010-9315-4

Cited by 27 publications

(20 citation statements)

References 67 publications

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“…With increasing cell size, the variation coefficient of the calculated porosities decreases monotonously, proving that porosity values calculated for small cells should not be accepted. The representative elementary volume (REV, Bear, 1972) concerning porosity for the studied granite body can be defined by the cell size, where the variation coefficient becomes stable (M. Tóth and Vass, 2011). On this basis, the aforementioned calculations suggest a REV of ~50 m. For this grid net the average porosity is 1.62% with a maximum of ~6%.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Fracture network characterization using 1D and 2D data of the Mórágy Granite body, southern Hungary

Tóth

2018

Journal of Structural Geology

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Section: Accepted Manuscriptmentioning

confidence: 99%

“…One of the most essential features of a simulated fracture network is the size and spatial position of its communicating subsystems. In the applied software, they can be found using a properly optimized trial-anderror algorithm (M. Tóth and Vass, 2011).…”

Section: Fracture Network Modellingmentioning

confidence: 99%

Fracture network characterization using 1D and 2D data of the Mórágy Granite body, southern Hungary

Tóth

2018

Journal of Structural Geology

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“…The frequency distribution of fracture lengths is one of the most important numerical features of a fracture network (M. Tóth and Vass, 2011) and is usually different for different rock types in a reservoir. Therefore, the length frequency distribution was measured separately for brecciated and non-brecciated limestones.…”

Section: Length and Aperture Frequency Distributionmentioning

confidence: 99%

Characterization and DFN Modelling of the Fracture Network in a Mesozoic Karst Reservoir: Gomba Oilfield, Paleogene Basin, Central Hungary

Bauer

Tóth

2017

Journal of Petroleum Geology

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Reservoir rocks at Gomba oilfield, located in the Hungarian Paleogene Basin, include fractured and karstified Triassic carbonates with significant fluid storage potential. However little information is available about the fracture network in these carbonates which has led to production problems at Gomba. The purpose of this study is to investigate the relationship between the spatial distribution of high porosity zones in the carbonates and the microfracture system. For this evaluation, individual fractures were studied and used in a discrete fracture network (DFN) modelling exercise at two wells, Gomba‐1 and Gomba‐3. The investigation took place at two different scales. Fracture length and aperture distributions were derived at the micro‐scale (mm to cm), whereas fracture density and fracture orientation data were investigated at the reservoir scale. The results of both investigations were taken into consideration in the modelling. The results of the fracture network models were compared to reservoir lithologies which ranged from fractured carbonates to collapse breccias. Based on the results, it appears that the porosity associated with a significant proportion of the fractures was increased as a result of dissolution. Fracture distribution was not uniform along the studied well paths, and fracture orientation was chaotic at particular depth intervals. On the basis of the DFN models, three different fractured zones are predicted to occur in the reservoir, but only two of the zones have significant fracture porosity. Comparing these results with petrographic observations, an epigene karst phase is proposed which is older than (or the same age as) the time of hydrocarbon migration; karst‐related voids were therefore important pathways for fluid migration. The void system and karst caverns partly collapsed during subsequent burial, resulting in karst‐related traps. By analogy with modern cave systems, the karst cavern zones at Gomba probably extend horizontally rather than vertically, and are oriented NE‐SW parallel to major structural lineaments.

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“…The connectivity of a fracture group can be defined by the proportion of the interconnected fractures. It essentially depends on the D, E and the orientation values of the fracture network (M. Tóth and Vass, 2011). In this paper a fracture group was considered well-communicating if it contains more than 5% of all simulated fractures from the modeled volume.…”

Section: Datamentioning

confidence: 99%

“…The length distribution of the fractures is one of the most important numerical features of a fracture network (M. Tóth and Vass 2011), and it can be easily different for each rock type in a given reservoir. Therefore, we analysed the fracture length distribution separately for breccia and limestone.…”

Section: Length and Aperture Distribution Calculationmentioning

confidence: 99%

Relationship between micro-fracture network and karstification in recent (Mecsek) and paleokarst (Gomba) reservoirs

Bauer¹

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Relationship Between the Geometric Parameters of Rock Fractures, the Size of Percolation Clusters and REV

Cited by 27 publications

References 67 publications

Fracture network characterization using 1D and 2D data of the Mórágy Granite body, southern Hungary

Fracture network characterization using 1D and 2D data of the Mórágy Granite body, southern Hungary

Characterization and DFN Modelling of the Fracture Network in a Mesozoic Karst Reservoir: Gomba Oilfield, Paleogene Basin, Central Hungary

Relationship between micro-fracture network and karstification in recent (Mecsek) and paleokarst (Gomba) reservoirs

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