A Comparison of Methods for the Stochastic Simulation of Rock Fractures

Methods reported in the literature for rock fracture simulations include approaches based on stochastic geometry, multiple-point statistics and a combination of geostatistics for fracture density and object-based modelling for fracture geometries. The advantages and disadvantages of each of these approaches are discussed with examples. By way of review, the authors begin with the geostatistical indicator simulation method, based on the truncated–Gaussian algorithm; this is followed by multiple-point statistical simulation and then the stochastic geometry approach, which is based on marked point process simulation. A new approach, based on pluriGaussian structural simulation, is then introduced. The new approach incorporates in the simulation the spatial correlation between different sets of fractures, which in general, is very difficult, if not impossible, to accomplish in the three methods reviewed. Each simulation method is summarised together with detailed simulation procedures for each. A published two-dimensional fracture dataset is used as a means of assessing the performance of each simulation method and of demonstrating the concepts discussed in the text.     

Stochastic Fracture Propagation Modelling for Enhanced Geothermal Systems

Fractures and fracture networks are the fundamental components of enhanced geothermal systems and determine their technical and economic viability. A realistic fracture model that can adequately describe a fracture-stimulated reservoir is critical for subsequent flow and heat transfer analyses of the system. Fractures in these systems are essentially the product of hydraulic stimulations of the reservoir that, together with ground conditions and the local stress regime, determine how fractures are formed and propagated. This paper describes three methods for generating realistic fracture models for enhanced geothermal systems; two of them incorporate the fracture propagation process in the modelling and hence provide a stochastic fracture propagation model. The methods are: a Bayesian framework in the form of Markov Chain Monte Carlo simulation, an extended Random Sampling Consensus model and a Point and Surface Association Consensus model. The conditioning data used in these methods are seismic events recorded during fracture stimulation. Geodynamics’ Habanero reservoir in the Cooper Basin of South Australia is used as a case study to test these methods.     

Discrete element modelling of rock cutting: from ductile to brittle transition

It is well accepted that there is a transition of failure mode from ductile to brittle with increasing depth of cut during rock cutting process. Rock failure modes affect cutting efficiency, and knowledge of the failure transition is essential to the determination of optimum cutting parameters. The critical transition depth can be linked with rock properties. In this study, an attempt was made to model rock cutting process and to check the dependence of the critical failure mode transition depth on the brittleness of rock. For this purpose, dimensional analysis was first performed to establish the correlations between rock macro‐properties and micro‐parameters for discrete element simulations. Following the specimen calibration procedure, two types of synthetic rocks having approximately the same uniaxial compressive strength were generated as the synthetic specimens for simulating the rock cutting process. The first specimen was created using conventional model construction method with identical bond strengths between particles, giving rise to undesirably high indirect tensile strength. The second specimen was created using a proposed clustering algorithm such that the ratio between the tensile and compressive strength matches reasonably well with that of real rocks. The results of rock cutting simulations demonstrate that failure mode transition took place in both models, but for the clustered model the transition emerged at a shallower cutting depth. A further exploration was made to derive the critical depth for this transition based on the simulations performed on the clustered models. The derived relationship indicates that the critical transition depth decreases as strength ratio or brittleness of the rock increases. This provides a very useful tool for predicting the critical depth which can be used to help cutting tool design and cutting parameter optimisations. Copyright © 2015 John Wiley & Sons, Ltd.     

A Simplified Failure Criterion for Intact Rocks Based on Rock Type and Uniaxial Compressive Strength

The uniaxial compressive strength (UCS) of intact rock, which can be estimated using relatively straightforward and cost-effective techniques, is one of the most practical rock properties used in rock engineering. Thus, constitutive laws to represent the strength and behavior of (intact) rock frequently use it, along with additional intrinsic rock properties. Although triaxial tests can be employed to obtain best-fit failure criterion parameters that provide best strength predictions, they are more expensive and require time-consuming procedures; as a consequence, they are often not readily available at early stages of a project. Based on the analysis of an extensive triaxial test database for intact rocks, we propose a simplified empirical failure criterion in which rock strength at failure is expressed in terms of confining stress and UCS, with a new parameter which can be directly estimated from the UCS for a specified rock type in the absence of triaxial test data. Performance of the proposed failure criterion is then tested for validation against experimental data for eight rock types. The results show that strengths of intact rock estimated by the proposed failure criterion are in good agreement with experimental test data, with small discrepancies between estimated and measurements strengths. Therefore, the proposed criterion can be useful for preliminary (triaxial) strength estimation of intact rocks when triaxial tests data are not available.     

Orebody Modelling for Exploration: The Western Mineralisation, Broken Hill, NSW

The Western Mineralisation in the Broken Hill deposit was studied to identify the zonation sequence of lithogeochemical haloes along and across the strike of the orebody. Samples used are from 77 drill holes and the samples were assayed for Pb, Zn, Fe, S, Cu, Ag, Cd, Sb, Bi and As. Variogram analyses were calculated for all the elements and kriging was used to construct the 3D block model. Analysis of cross sections along and across the strike of the orebody shows that Bi and Sb form broader halos around sulphide masses and this suggests that they are pathfinder elements for the Pb and Zn elements of this orebody. The threshold concentrations (minimum anomaly) of the 10 elements were determined using the concentration-area analysis. On east?Cwest vertical cross sections, the values of linear productivity, variability gradient and zonality index were calculated for each element. Based on the maximum zonality index of each element, the sequence of geochemical zonation pattern was determined from top to bottom of the orebody. The result shows that S, Pb, Zn and Cd tend to concentrate in the upper part of the mineralisation whereas Ag, Cu, Bi and As have a tendency to concentrate in the lower part of the mineralised rocks. Also, an empirical product ratio index was developed based on the position of the elements in the zonation sequence. The methods and results of this research are applicable to exploration of similar Zn and Pb sulphide ore deposits.     

A Novel Index for Quantifying Small-Scale Resource Heterogeneity

Mathematical Geosciences - The objective of the work presented in this paper is to provide a quantitative measure of grade heterogeneity at the selective mining unit scale within relatively large...     

A particle mechanics approach for the dynamic strength model of the jointed rock mass considering the joint orientation

In nature, there exist several forms of anisotropy in rock masses due to the presence of bedding planes, joints, and weak layers. It is well understood that the anisotropic properties of jointed rock masses significantly affect the stability of surface and underground excavations. However, these critical anisotropic characteristics are often ignored in existing uniaxial dynamic failure criteria. This study investigates the effect of a pre-existing persistent joint on the rate-dependent mechanical behaviours of a rock mass using a particle mechanics approach, namely, bonded particle model (BPM), to realistically replicate the mechanical response of the rock mass. Firstly, in order to capture the rate-dependent response of the jointed rock mass, the BPM model is validated using published experimental data. Then, a dynamic strength model is proposed based on the Jaeger criterion and simulation results. To further investigate the dynamic behaviours, the dynamic uniaxial compressive strength (UCS) for anisotropic rock masses with various joint orientations is investigated by subjecting the BPM models to uniaxial compression numerical tests with various strain rate. The proposed dynamic strength model is validated based on numerical simulation results. Finally, the fragmentation characteristics of the jointed rock masses are analysed, which demonstrate that the failure mode affects the dynamic UCS. This is further confirmed by the analysis of the orientations of microscopic cracks generated by the compression loading.     

Improving Coal Quality Estimations with Geostatistics and Geophysical Logs

In most modern coal mines, there are many coal quality parameters that are measured on samples taken from boreholes. These data are used to generate spatial models of the coal quality parameters, typically using inverse distance as an interpolation method. At the same time, downhole geophysical logging of numerous additional boreholes is used to measure various physical properties but no coal quality samples are taken. The work presented in this paper uses two of the most important coal quality variables—ash and volatile matter—and assesses the efficacy of using a number of geostatistical interpolation methods to improve the accuracy of the interpolated models, including the use of auxiliary variables from geophysical logs. A multivariate spatial statistical analysis of ash, volatile matter and several auxiliary variables is used to establish a co-regionalization model that relates all of the variables as manifestations of an underlying geological characteristic. A case study of a coal mine in Queensland, Australia, is used to compare the interpolation methods of inverse distance to ordinary kriging, universal kriging, co-kriging, regression kriging and kriging with an external drift. The relative merits of these six methods are compared using the mean error and the root mean square error as measures of bias and accuracy. The study demonstrates that there is significant opportunity to improve the estimations of coal quality when using kriging with an external drift. The results show that when using the depth of a sample as an external drift variable there is a significant improvement in the accuracy of estimation for volatile matter, and when using wireline density logs as the drift variable there is improvement in the estimation of the in situ ash. The economic benefit of these findings is that cheaper proxies for coal quality parameters can significantly increase data density and the quality of estimations.