Semi-automated component identification of a complex fracture network using a mixture of von Mises distributions: Application to the Ardeche margin (South-East France)

Chabani, Arezki; Mehl, Caroline; Cojan, Isabelle; Alais, Robin; Bruel, Dominique

doi:10.1016/j.cageo.2020.104435

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Then, we are able to check the best number of fracture sets from the distributions using the goodness of fit parameters (e.g., Likelihood). For more details, see [62] who describe and adapt the methodology for structural data. The standard of deviation of +/−10 • was calculated for each given mean orientation value in this study.…”

Section: Fractures Analysismentioning

confidence: 99%

“…The length of each arrow corresponds to the fracture set abundance. The horizontal line above each arrow corresponds to the fracture set standard deviation within an interval of confidence of 75% (see[62] for more explanations). Each color arrow corresponds to fracture set: Blue arrow corresponds to NNE/SSW fracture set, red arrow corresponds to the NE/SW fracture set, green arrow corresponds to the E/W fracture set, yellow arrow corresponds to the NNW/SSE fracture set, purple arrow corresponds to the NW/SE fracture set, and marron arrow corresponds to the N/S fracture set.…”

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confidence: 99%

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Multiscale Characterization of Fracture Patterns: A Case Study of the Noble Hills Range (Death Valley, CA, USA), Application to Geothermal Reservoirs

et al. 2021

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In the basement fractured reservoirs, geometric parameters of fractures constitute the main properties for modeling and prediction of reservoir behavior and then fluid flow. This study aims to propose geometric description and quantify the multiscale network organization and its effect on connectivity using a wide-ranging scale analysis and orders scale classification. This work takes place in the Noble Hills (NH) range, located in the Death Valley (DV, USA). The statistical analyses were performed from regional maps to thin sections. The combination of the length datasets has led to compute a power law exponent around −2, meaning that the connectivity is ruled by the small and the large fractures. Three domains have been highlighted in the NH: (1) domain A is characterized by a dominance of the NW/SE direction at the fourth order scale; (2) domain B is characterized by a dominance of the E/W and the NW/SE directions at respectively the fourth and third order scales; (3) domain C is also marked by the E/W direction dominance followed by the NW/SE direction respectively at the fourth and third order scale. The numerical simulations should consider that the orientation depends on scale observation, while the length is independent of scale observation.

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Section: Fractures Analysismentioning

confidence: 99%

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confidence: 99%

Multiscale Characterization of Fracture Patterns: A Case Study of the Noble Hills Range (Death Valley, CA, USA), Application to Geothermal Reservoirs

et al. 2021

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“…In our study, a MvM distributions with 18 components was used to characterize fracture orientation. However, this can be further improved by application of "BAMBI" R software package and the Python code described by Chabani et al (2020). Furthermore, the approach described by Tran (2007) can be further applied to characterize fracture orientation and minimize statistical errors that can have an important impact on numerical models.…”

Section: Discussionmentioning

confidence: 99%

Impact of fracture properties on the performance of engineered geothermal systems in the crystalline basement of Kuujjuaq (Canadian Shield)

Miranda

Raymond

Dezayes

et al. 2023

Preprint

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Understanding the natural fracture network is essential for geothermal-related investigations. However, the geometrical attributes depend on the scale of observation. Therefore, a multiscale characterization of the fracture network is essential to ensure that forward heat and flow simulations are based on stochastically generated discrete fracture network models representative of the natural fracture system observed. This was the goal of this work. Fracture data was collected from satellite imagery, outcrops and well cores to evaluate the scale effect and to study the impact of fracture size and density on the performance of engineered geothermal systems by numerical modeling. The numerical simulations highlighted that networks made of small fractures (0.08 to 27 m) tend to decrease the performance of the system compared to a network made of large fractures (22 to 1,437 m). However, thermal short-circuiting is easily reached in the latter scenario. Thus, the simulations suggest that the best-case network is made of fractures ranging between 1.57 to 135 m with fractures spaced by 5 m. This scenario provides the best compromise between heat extraction, water losses, hydraulic impedance and thermal drawdown. Despite the uncertainties, the fracture data used highlights the importance of multiscale fracture analysis for heat-flow simulations of geothermal reservoirs.

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“…Determining the weights and parameters of each component distribution within the model necessitates estimating them using methods like maximum likelihood estimation or Bayesian inference [84]. Moreover, the MvM is widely used in various applications, including modeling biological rhythms, analyzing directional data in environmental sciences, and clustering circular data in machine learning [85,86,43,78]. By capturing the underlying patterns and structure in directional datasets, the MvM provides a flexible and powerful tool for understanding and modeling complex directional phenomena.…”

Section: Mixture Of Von Mises Distributionmentioning

confidence: 99%

In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions

Benlakhdar,

Rziza,

Oulad Haj Thami

2024

Stat., optim. inf. comput.

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Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises' seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references pertinent to von Mises distribution models.

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Semi-automated component identification of a complex fracture network using a mixture of von Mises distributions: Application to the Ardeche margin (South-East France)

Cited by 5 publications

References 26 publications

Multiscale Characterization of Fracture Patterns: A Case Study of the Noble Hills Range (Death Valley, CA, USA), Application to Geothermal Reservoirs

Multiscale Characterization of Fracture Patterns: A Case Study of the Noble Hills Range (Death Valley, CA, USA), Application to Geothermal Reservoirs

Impact of fracture properties on the performance of engineered geothermal systems in the crystalline basement of Kuujjuaq (Canadian Shield)

In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions

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