A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system

Abstract

Fracture network developed in hot dry rock (HDR) primarily controls the flow and heat transport in enhanced geothermal system (EGS). Characterization of fracture network in deep geothermal reservoir is among the most challenging tasks. An interpretation framework for explicit delineating complex fracture network is proposed based on induced micro-seismic events, hydraulic stimulation and tracer test data, which are often available in EGS sites. It is processed by (1) the spatial distribution of fracture intensity, size and hydraulic aperture constrained by the hydraulic diffusivity derived from the spatio-temporal distribution of micro-seismic events, (2) numerical models and long short-term memory (LSTM) neural network models for the hydraulic stimulation and tracer test, and (3) parameter inversion model based on the multi-objective Harris hawks optimization (MOHHO) algorithm searching for optimal fracture network parameters minimizing the deviations between model predictions and field observations. This method has been applied to the Habanero EGS site, Australia. The prediction accuracy of the injection wellhead pressure and produced tracer concentration of the inversed micro-seismicity mapping DFN model is improved by 61.16% and 67.35%, respectively, compared with the stochastic DFN model. These results emphasize the importance to integrate induced micro-seismicity monitoring data for the characterization of hydraulically stimulated fracture network.