Characterization and modeling of a naturally fractured granitic reservoir : application to the geothermic reservoir of Soultz-sous-Forets (France).

Benoit Massart and Marie Paillet and Vincent Henrion and Judith Sausse. ( 2009 )
in: Proc. 29th Gocad Meeting, Nancy

Abstract

Fractures and faults represent an important problematic of reservoir studies because they lead to a source of high incertitudes during the process of flow modeling. They constitute the main drains especially in low matrix porosity rocks and therefore, they induce variations of the flow which may highly affects the predictability of reservoir models. The characterization and the modeling of the fractures set is necessary to describe the networks geometry and thus determinate the connectivity of the fractures from well to well and within the reservoir. In the specific case of the Soultz-sous-Forˆts geothermal reservoir, a new statistical analysis of e the fault and fracture networks is proposed to precise the actual structural model of the reservoir. The statistical characterization of the fractures and faults is realized with the re-interpretation of the entire borehole images database (U.B.I.). 1800 fractures are determined along the three deep Soultz well paths, grouped into main conjugates fractures sets, showing a mean N-S orientation and a mean dip of 70˚, that is coincident with the Oligocene N-S extension responsible of the formation of the Rhine graben. A correlation between the geometric parameters of fractures, aperture, width and size is proposed. Widths W and extensions L of fractures follow a power-law type correlation of the form L = k · W D with k a coefficient characteristic of the facies and D the fractal dimension of the fracture set. These parameters are used to determine the volumetric density of fractures (number of fractures / m3 ) at the wells scale. Finally, this denisty and the statistics of fracture properties are used to constrain stochastic simulation of discrete fracture network (DFN).

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BibTeX Reference

@INPROCEEDINGS{MassartGM2009,
    author = { Massart, Benoit and Paillet, Marie and Henrion, Vincent and Sausse, Judith },
     title = { Characterization and modeling of a naturally fractured granitic reservoir : application to the geothermic reservoir of Soultz-sous-Forets (France). },
 booktitle = { Proc. 29th Gocad Meeting, Nancy },
      year = { 2009 },
  abstract = { Fractures and faults represent an important problematic of reservoir studies because they lead to a source of high incertitudes during the process of flow modeling. They constitute the main drains especially in low matrix porosity rocks and therefore, they induce variations of the flow which may highly affects the predictability of reservoir models. The characterization and the modeling of the fractures set is necessary to describe the networks geometry and thus determinate the connectivity of the fractures from well to well and within the reservoir. In the specific case of the Soultz-sous-Forˆts geothermal reservoir, a new statistical analysis of e the fault and fracture networks is proposed to precise the actual structural model of the reservoir. The statistical characterization of the fractures and faults is realized with the re-interpretation of the entire borehole images database (U.B.I.). 1800 fractures are determined along the three deep Soultz well paths, grouped into main conjugates fractures sets, showing a mean N-S orientation and a mean dip of 70˚, that is coincident with the Oligocene N-S extension responsible of the formation of the Rhine graben. A correlation between the geometric parameters of fractures, aperture, width and size is proposed. Widths W and extensions L of fractures follow a power-law type correlation of the form L = k · W D with k a coefficient characteristic of the facies and D the fractal dimension of the fracture set. These parameters are used to determine the volumetric density of fractures (number of fractures / m3 ) at the wells scale. Finally, this denisty and the statistics of fracture properties are used to constrain stochastic simulation of discrete fracture network (DFN). }
}