Geometric properties and scaling laws of the fracture network of the Ypresian carbonate reservoir in central Tunisia: Examples of Jebels Ousselat and Jebil

Ammari, Anis; Abbes, Chedly; Abida, Habib

doi:10.1016/j.jafrearsci.2022.104718

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…Both FD distributions follow a negative power law distribution with somewhat similar exponents. Petrologically speak-ing, different rock bodies usually react differently during deformation, which could cause fracture networks of different geometric and hydrodynamic features [90,91]. However, this study shows that although SG and OG exhibit slightly different metamorphic evolutions and mineralogy, they have similar fracture types in terms of fracture density.…”

Section: Theoretical Concept Of the Fracture Network Of The Studied Areamentioning

confidence: 66%

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

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Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

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Section: Theoretical Concept Of the Fracture Network Of The Studied Areamentioning

confidence: 66%

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

View full text Add to dashboard Cite

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“…However, two dimension numbers (d and D) are often insufficient to properly characterize the striking properties of the fractal network. In fact, networks with the same similarity dimension D > d can have very different topological, morphological, and metrological properties [41][42][43][44]. Hence, in order to unambiguously quantify the fractal features, one needs to employ more dimension numbers.…”

Section: Introductionmentioning

confidence: 99%

Fractal Features of Fracture Networks and Key Attributes of Their Models

et al. 2023

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This work is devoted to the modeling of fracture networks. The main attention is focused on the fractal features of the fracture systems in geological formations and reservoirs. Two new kinds of fracture network models are introduced. The first is based on the Bernoulli percolation of straight slots in regular lattices. The second explores the site percolation in scale-free networks embedded in the two- and three-dimensional lattices. The key attributes of the model fracture networks are sketched. Surprisingly, we found that the number of effective spatial degrees of freedom of the scale-free fracture network models is determined by the network embedding dimension and does not depend on the degree distribution. The effects of degree distribution on the other fractal features of the model fracture networks are scrutinized.

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Machine learning for prediction of the uniaxial compressive strength within carbonate rocks

et al. 2023

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Geometric properties and scaling laws of the fracture network of the Ypresian carbonate reservoir in central Tunisia: Examples of Jebels Ousselat and Jebil

Cited by 3 publications

References 67 publications

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Fractal Features of Fracture Networks and Key Attributes of Their Models

Machine learning for prediction of the uniaxial compressive strength within carbonate rocks

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