This paper describes a method to provide information about fracture topology in geothermal reservoirs using Electrical Resistivity Tomography (ERT). Fracture characterization in Enhanced Geothermal Systems (EGS) is crucial to ensure adequate supply of geothermal fluids and efficient thermal operation of the wells. The knowledge of fluid-flow patterns in the reservoir helps preventing short-circuiting flow paths from injector to producer that would lead to premature thermal breakthrough. The resistivity distribution of a field can be estimated by measuring potential differences between various points while injecting an electric current into the ground, and resistivity data can be used to infer fracture properties due to the large contrast in resistivity between water and rock. The inverse method requires a large parameter space and can be hard to solve so it is important to find ways to improve the fracture characterization process. In this study, the contrast between rock and fractures was enhanced by injecting a conductive tracer into the reservoir, thereby decreasing the resistivity of the fractures as the fluid flows through the fracture network. The time history of the potential difference between two points (an injector and a producer), which corresponds to the resistivity changes, depends on the fracture network and therefore helps estimate where fractures are located and the character of their distribution. The flow simulator TOUGH2 was used to calculate how the conductive tracer distributes through the reservoir and the analogy between Ohm's law that describes electrical flow and Darcy's law describing fluid flow made it possible to use TOUGH2 also to calculate the electric fields. The EOS1 module in TOUGH2 was used to calculate the tracer flow and EOS9 module was used to calculate the electric potential. First, the electric potential calculated using the ESO9 module was verified by comparing the results to an analytical solution. Next, the time history of the potential difference between an injector and a producer was calculated for two simple fracture networks to explore the relationship between fracture networks and the changes in potential field. The time histories of the potential difference was also studied for more realistic fracture networks by using a discrete-fracture model introduced by Karimi-Fard et al. (2003) to create more complicated fracture networks. The method used is based on an unstructured control volume finite-difference formulation where the cell connections are defined using a connectivity list. Four cases were studied and they all gave different results for the time histories of the potential difference, verifying that the potential field is dependent on the fracture networks. By studying this relationship further the changes in potential fields could be used to provide information about the fractures characteristics.

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