3D Numerical Simulation of Elastic Wave Propagation in Discrete Fracture Network Rocks
- 195 Downloads
Fractures play an important role in controlling rock block stability and the hydraulic properties of fractured rock formations. Understanding elastic wave propagation in fractured media can result in significant advances for the geophysical prediction of fracture parameters from seismic data. However, most natural fracture characteristics, such as fracture length, aperture, angle and location are random; therefore, fracture models must be built discretely and follow some stochastic principles. We construct stochastic models of fractured rock samples using a random fracture network rather than a single fracture. Three-dimensional (3D) wave field computation is a computationally complex problem. Here, the 3D fourth-order in space, second-order in time, displacement-stress staggered-grid finite-difference scheme is used for accurate simulations. Our numerical examples demonstrate the effects of varying fracture number, aperture, and length distribution of the fracture network on the seismic response. The wave field scattering caused by the contrast between fractures and background media is one of the key features, and the resulting scattering is more obvious for S-waves than for P-waves. Such an approach can be applied to any fracture network model that provides a link between fracture parameters and seismic attributes.
KeywordsWave propagation discrete fracture network scattering fracture parameter
This research is supported by “the Fundamental Research Funds for the Central Universities” (2018ZDPY08) and the National Science Foundation of China (NSFC grant no. 41474122). We would like to thank Dr. Younes Fadakar Alghalandis for providing ADFNE software for discrete fracture modeling.
- Hilloulin, B., et al. (2016). Monitoring of autogenous crack healing in cementitious materials by the nonlinear modulation of ultrasonic coda waves, 3D microscopy and X-ray microtomography. Construction and Building Materials,123, 143–152. https://doi.org/10.1016/j.conbuildmat.2016.06.138.CrossRefGoogle Scholar
- Hunziker, J., Favino, M., Caspari, E., Quintal, B., Rubino, J. G., Krause, R., et al. (2018). Seismic attenuation and stiffness modulus dispersion in porous rocks containing stochastic fracture networks. Journal of Geophysical Research: Solid Earth,123, 125–143. https://doi.org/10.1002/2017jb014566.CrossRefGoogle Scholar
- Monsalve, J. J., Baggett, J., Bishop, R., & Ripepi, N. (2019). Application of laser scanning for rock mass characterization and discrete fracture network generation in an underground limestone mine. International Journal of Mining Science and Technology,29, 131–137. https://doi.org/10.1016/j.ijmst.2018.11.009.CrossRefGoogle Scholar
- Morasch, A., Reeb, A., Baier, H., Weidenmann, K. A., & Schulze, V. (2015). Characterization of debonding strength in steel-wire-reinforced aluminum and its influence on material fracture. Engineering Fracture Mechanics,141, 242–259. https://doi.org/10.1016/j.engfracmech.2015.05.029.CrossRefGoogle Scholar
- Osinowo, O. O., Chapman, M., Bell, R., & Lynn, H. B. (2017). Modelling orthorhombic anisotropic effects for reservoir fracture characterization of a naturally fractured tight carbonate reservoir, Onshore Texas, USA. Pure and Applied Geophysics,174, 4137–4152. https://doi.org/10.1007/s00024-017-1620-0.CrossRefGoogle Scholar
- Vlastos, S., Liu, E., Main, I. G., & Li, X. Y. (2003). Numerical simulation of wave propagation in media with discrete distributions of fractures: effects of fracture sizes and spatial distributions. Geophysical Journal International,152, 649–668. https://doi.org/10.1046/j.1365-246X.2003.01876.x.CrossRefGoogle Scholar