Induced microearthquakes predict permeability creation in the brittle crust - Li, Ziyan; Elsworth, Derek; Wang, Chaoyi; , EGS-Collab; Ajo-Franklin, J.; Baumgartner, T.; Beckers, K.; Blankenship, D.; Bonneville, A.; Boyd, L.; Brown, S.; Burghardt, J.A.; et al. (Frontiers in Earth Science, 2022)
Predicting the evolution of permeability accurately during stimulation at reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here we develop a hybrid machine learning (ML) model to visualize in-situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes a ML model that trained using the well history of flow rate and wellhead pressure and MEQs data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physical inspired model is integrated to estimate in-situ fracture permeability spatially. This method link fracture permeability to fracture dilation and scaling dilation to equivalent MEQ magnitude by laboratory observations. The seismic data are then applied define incremental changes in permeability in both space and in time. Our results confirm the excellent agreement between ground truth and model predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.