Younes Fadakar Alghalandis

The RANSAC Method for Generating Fracture Networks from Micro-seismic Event Data

Fracture network modeling is an essential part of the design, development and performance assessment of Enhanced Geothermal Systems. These systems are created from geothermal resources, usually located several kilometers below the surface of the Earth, by establishing a network of connected fractures through which fluid can flow. The depth of the reservoir makes it impossible to make direct measurements of fractures and data are collected from indirect measurements such as geophysical surveys. An important source of indirect data is the seismic event point cloud generated by the fracture stimulation process. Locations of these points are estimated from recorded micro-seismic signals generated by fracture initiation, propagation and slip. This point cloud can be expressed as a set of three-dimensional coordinates with attributes, for example Seijk={(x,y,z); a|x,y,z∈R, a∈I}. We describe two methods for reconstructing realistic fracture trace lines and planes given the point cloud of seismic events data: Enhanced Brute-Force Search and RANSAC. The methods have been tested on a synthetic data set and on the Habanero data set of Geodynamics’ geothermal project in the Cooper Basin of South Australia. Our results show that the RANSAC method is an efficient and suitable method for the conditional simulation of fracture networks.

Connectivity Field: a Measure for Characterising Fracture Networks

Analysis of the connectivity of a fracture network is an important component of the design, assessment and development of fracture-based reservoirs in geothermal, petroleum and groundwater resource applications. It is a useful means of characterising the flow pathways and the mechanical behaviours of reservoirs. An appropriate practical measure is required for connectivity characterisation because of the extreme complexity of fracture networks. In this paper, we propose the connectivity field (CF), as a useful measure to evaluate the spatial connectivity characteristics of fractures in a fracture network. The CF can be applied on both a particular realisation of a fracture network model (for deterministic evaluation) and on stochastic fracture network models using stochastic modelling and Monte Carlo simulations (for probabilistic evaluation with uncertainties). Two extensions are also proposed: the generalised connectivity field, a measure that is independent of support size, and the probabilistic connectivity field. Potential applications of the CF and its extensions are in determining the optimal location of an injection or production well so as to maximise reservoir performance and in determining potential flow pathways in fracture networks. The average CF map shows strong correlations with the

Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran

X_{\mathrm{f}}

Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran

Xf and P21 measures. The relationships between the CF measures, the fracture intersection density and the fracture network connectivity index are also investigated.