Simulation-based investigation on the accuracy of discrete fracture network (DFN) representation

Abstract Calibration and validation of the Discrete Fracture Network (DFN) model are always based on the comparison between actual fracture trace mapping and cross-section extracted from the generated model, and some inaccuracies may arise. In this study, the process of fracture data acquisition was simulated; then, the accuracy of the DFN representation technique was investigated. A DFN model (called the original DFN (ODFN) model) was generated based on a natural fracture network. Sampling windows were set inside the ODFN model, and fracture data were collected. A number of additional DFN models, defined as the secondarily constructed DFN (SCDFN) models, were developed. The geometric and mechanical properties of the ODFN and SCDFN models were determined, comparisons between the ODFN and SCDFN models were performed, and the accuracy of the DFN representation technique was analyzed. The results show that the fluctuations in the geometrical properties of these SCDFN models are insignificant and that the mechanical properties of a few SCDFN models considerably differ from those of the ODFN model. These findings indicate that DFN representation is a considerably robust method of characterizing the fracture patterns of in situ rock masses, but care must be exercised when this technique is used in engineering practice.

[1]  O. Stephansson,et al.  Use of the distinct element method to perform stress analysis in rock with non-persistent joints and to study the effect of joint geometry parameters on the strength and deformability of rock masses , 1992 .

[2]  Pinnaduwa Kulatilake,et al.  Sampling bias on orientation of discontinuities , 1984 .

[3]  Yinhe Zheng,et al.  Estimation of the REV size for blockiness of fractured rock masses , 2016 .

[4]  K. Farahmand,et al.  Investigating the scale-dependency of the geometrical and mechanical properties of a moderately jointed rock using a synthetic rock mass (SRM) approach , 2018 .

[5]  Zhiye Zhao,et al.  Geological discontinuity persistence: Implications and quantification , 2018, Engineering Geology.

[6]  E. T. Brown,et al.  Rock Mechanics: For Underground Mining , 1985 .

[7]  Qingfa Chen,et al.  Integration of homogeneous structural region identification and rock mass quality classification , 2019, Royal Society Open Science.

[8]  A. Baghbanan,et al.  Numerical determination of deformability and strength of 3D fractured rock mass , 2018, International Journal of Rock Mechanics and Mining Sciences.

[9]  X. Zhuang,et al.  Estimation of the fracture diameter distributions using the maximum entropy principle , 2014 .

[10]  Arild Palmström,et al.  Measurements of and correlations between block size and rock quality designation (RQD) , 2005 .

[11]  Pinnaduwa Kulatilake,et al.  Estimation of rock mass strength and deformability in 3-D for a 30 m cube at a depth of 485 m at Äspö Hard Rock Laboratory , 2004 .

[12]  N. Odling,et al.  Scaling of fracture systems in geological media , 2001 .

[13]  M. Mauldon,et al.  Estimating Mean Fracture Trace Length and Density from Observations in Convex Windows , 1998 .

[14]  Qing Wang,et al.  Determination of structural domain boundaries in jointed rock masses: An example from the Songta dam site, China , 2014 .

[15]  Pinnaduwa H.S.W. Kulatilake,et al.  REV and its properties on fracture system and mechanical properties, and an orthotropic constitutive model for a jointed rock mass in a dam site in China , 2012 .

[16]  Variance of non-parametric rock fracture mean trace length estimator , 2010 .

[17]  Pinnaduwa Kulatilake,et al.  The density of discontinuity traces in sampling windows , 1984 .

[18]  J. Hadjigeorgiou,et al.  Influence of Stope Excavation on Drift Convergence and Support Behavior: Insights from 3D Continuum and Discontinuum Models , 2018, Rock Mechanics and Rock Engineering.

[19]  J. Thovert,et al.  Permeability and percolation of anisotropic three-dimensional fracture networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  J. Bear Dynamics of Fluids in Porous Media , 1975 .

[21]  Z. T. Bieniawski,et al.  Engineering Rock Mass Classifications: A Complete Manual for Engineers and Geologists in Mining, Civil, and Petroleum Engineering , 1989 .

[22]  Lianyang Zhang,et al.  Estimating the intensity of rock discontinuities , 2000 .

[23]  Scaling solutions for connectivity and conductivity of continuous random networks. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Xudong Han,et al.  A 3D Fracture Network Model for the Undisturbed Rock Mass at the Songta Dam Site Based on Small Samples , 2016, Rock Mechanics and Rock Engineering.

[25]  D. Jiang,et al.  Physical simulation of construction and control of two butted-well horizontal cavern energy storage using large molded rock salt specimens , 2019, Energy.

[26]  T. G. Carter,et al.  A new and unified approach to improved scalability and volumetric fracture intensity quantification for GSI and rockmass strength and deformability estimation , 2018, International Journal of Rock Mechanics and Mining Sciences.

[27]  F. Agliardi,et al.  Structurally-controlled instability, damage and slope failure in a porphyry rock mass , 2013 .

[28]  L. J. Lorig,et al.  Application of discrete fracture networks in mining and civil geomechanics , 2015 .

[29]  S. R. Hencher,et al.  Forensic Excavation of Rock Masses: A Technique to Investigate Discontinuity Persistence , 2017, Rock Mechanics and Rock Engineering.

[30]  Jianhui Deng,et al.  An improved Monte Carlo simulation method for discontinuity orientations based on Fisher distribution and its program implementation , 2014 .

[31]  Chung-In Lee,et al.  Estimation of joint length distribution using window sampling , 2001 .

[32]  Pinnaduwa Kulatilake,et al.  Estimation of mean trace length of discontinuities , 1984 .

[33]  Herbert H. Einstein,et al.  Characterizing rock joint geometry with joint system models , 1988 .