Fracture network characterization in reservoirs by joint inversion of microseismicity and thermal breakthrough data: Method development and verification

Abstract

The spatial configuration of fractures often regulates flow and transport in the subsurface. However, the characterization of fracture networks is a challenging task, especially in deep reservoirs, because here only a limited number of boreholes is available to perform downhole logs and cross‐well hydraulic and tracer tests. In this study, we develop a joint inversion procedure to infer fracture number, geometry, and aperture based on Bayesian principles, utilizing microseismic (MS) events and thermal breakthrough data; both are typically available in enhanced geothermal systems and unconventional gas/oil reservoirs. A basic discrete fracture geometry, including orientations, lengths and positions of fractures, is generated from an MS events cloud by minimizing the distance between fractures and MS events. This geometric configuration, together with the aperture, is then further adjusted, based on the reversible jump Markov chain Monte Carlo algorithm, to minimize the misfit between observed and calculated thermal breakthrough curves by iterative forward flow and heat transport modeling. The inversion model is applied to two synthetic test cases. Following the sensitivity analysis of temperatures to fracture parameters, and the robustness analysis on the model performance under supportive data featuring good and poor quality, it is confirmed that the probabilistic joint inversion procedure approximates the fracture geometry and aperture well, and errors in predicting temperatures based on realizations of fracture networks are well below 5%. The methodology provides a new way to characterize fracture networks in the subsurface, without restrictions on predetermined fracture sizes or the number of fractures.