Hydraulic fracturing is usually employed to create a complex fracture network to enhance heat extraction in the development of an enhanced geothermal system. The heat extraction depends on the heat conduction from the rock matrix to the flowing fractures and the heat convection through a complex fracture network. Therefore, the geometries of the fracture network have important influences on the thermal breakthrough. In this paper, a hydro-thermal coupling mathematical model considering a complex fracture network is established. The embedded discrete fracture model is adopted to explicitly model the individual fracture on the mass flow and heat transfer. The model is validated by analytical solutions. Fracture network parameters are changed systematically to investigate the effects of fracture network distribution including regular and complex shape on the thermal production performance. The results show that the increase of producing pressure differential, fracture number, and conductivity will cause an early thermal breakthrough. The strong variation in fracture conductivity, as well as spacing and orientation, will cause thermal flow channeling and decrease the efficiency of heat extraction. A modified connectivity field is proposed to characterize the spatial variation of fracture network connectivity, which can be used to infer the thermal flow path.

Ranging has been regarded as one of the fundamental enabling technologies for a multitude of applications that require high accurate position information, such as automated navigation, vehicle platooning, asset management, etc. Among various ranging techniques, impulse-radio ultra-wideband is one of the most competitive technologies for high-precision ranging, because of its capability of achieving centimeter-level ranging accuracy, even for dense urban, indoor or cave like environments. However, two main challenges arise when fully exploiting the ranging capability of impulse-radio ultrawideband: (i) the extremely high sampling rate to acquire the received multipath signal, and (ii) the optimal thresholding strategy to differentiate the first path. To efficiently tackle those challenges, in this work, we propose a ranging approach under the compressed sensing framework. Specifically, the received ranging signal is acquired by low-rate compressed sampling through parallel random projections. Then, an algorithm named matching-pursuit search-back is proposed to detect the first arrival path, which integrates a backward iterative search and thresholding process starting from the peak path. The detection threshold is dynamically adjusted in each iteration to asymptotically minimize the averaged detection errors over false alarm and missed detection. Extensive simulations and experiments with field data are provided to demonstrate that the proposed approach can achieve high-precision ranging with far fewer samples compared with the traditional Nyquist-sampling based ones.

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