A simple genetic algorithm for calibration of stochastic rock discontinuity networks

Este artículo propone un método para llevar a cabo la calibración de las familias de discontinuidades en macizos rocosos. We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such back-calculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications

Automatic Mapping of Discontinuity Persistence on Rock Masses Using 3D Point Clouds

Finding new ways to quantify discontinuity persistence values in rock masses in an automatic or semi-automatic manner is a considerable challenge, as an alternative to the use of traditional methods based on measuring patches or traces with tapes. Remote sensing techniques potentially provide new ways of analysing visible data from the rock mass. This work presents a methodology for the automatic mapping of discontinuity persistence on rock masses, using 3D point clouds. The method proposed herein starts by clustering points that belong to patches of a given discontinuity. Coplanar clusters are then merged into a single group of points. Persistence is measured in the directions of the dip and strike for each coplanar set of points, resulting in the extraction of the length of the maximum chord and the area of the convex hull. The proposed approach is implemented in a graphic interface with open source software. Three case studies are utilized to illustrate the methodology: (1) small-scale laboratory setup consisting of a regular distribution of cubes with similar dimensions, (2) more complex geometry consisting of a real rock mass surface in an excavated cavern and (3) slope with persistent sub-vertical discontinuities. Results presented good agreement with field measurements, validating the methodology. Complexities and difficulties related to the method (e.g. natural discontinuity waviness) are reported and discussed. An assessment on the applicability of the method to the 3D point cloud is also presented. Utilization of remote sensing data for a more objective characterization of the persistence of planar discontinuities affecting rock masses is highlighted herein

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 ChainMonte 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.Chaoshui Xu; Peter A. Dow