Joint Monitoring of Acoustic Emission and Fracture Geometry in Glutenite Hydraulic Fracturing Based on High-Frequency and Low-Frequency Das Data

Hydraulic fractures are typically caused by the combined effects of external and in-situ stresses and manifest as a complex fracture networks of natural and artificial fractures (Chen et al., 2021; Schultz et al., 2020). However, these fractures are difficult to detect and observe in reservoirs for lack of full complete three-dimensional velocity model and monitoring system. Acoustic emission events (AEs) are defined as transient elastic waves that characterize the nucleation and evolution of rock fractures during laboratory-scale hydraulic fracturing (Li et al., 2023). Piezoelectric transducers (PZTs) are typically used to detect this motion and convert it into an electrical signal, which can then be used for AE localization. However, since the voltage cannot reflect the stress state of the fractures, using PZTs alone cannot simultaneously monitor AE activity and characterize crack features. Distributed Acoustic Sensing (DAS) is a revolutionary seismic monitoring technology that converts fiber-optic cables into dense sensors to detect and monitor a wide range of vibrations (Daley et al., 2013; Lindsey and Martin, 2021), including acoustic waves, seismic waves and even ambient noise. The operation of DAS is relatively simple: the interrogator unit (IU) emits laser pluses and receive backscattered Rayleigh light from the cables for measurement. In addition, DAS requires no electronic devices in the sensing cables and is resistant to high temperatures and corrosion, As a result, it has been widely applied in various fields, including oil and gas development, geothermal reservoir monitor, civil engineering, and smart city infrastructure. While Distributed Acoustic Sensing (DAS) has proven to be a powerful tool for independently monitoring acoustic emission (AE) events and characterizing fracture behavior during hydraulic fracturing, integrated analyses remain relatively limited. Hull et al. (2017) demonstrated a spatial and temporal correlation between microseismicity and DAS-recorded strain along the fiber, suggesting that the strain deformation boundary effectively constrains the extent of induced seismicity. Similarly, Starr and Jacobi (2017) reported a significant association between seismic locations and relative strain, with most microseismic events triggering measurable strain perturbations. Li et al. (2020) identified multiple new tensile-mode fractures from low-frequency DAS strain signals, and sebsequently converted these strain responses into microseismic signals. Their results revealed a strong spatial correlation between strain-inferred fractures and microseismic locations, providing compelling evidence of strain-induced fracture formation. However, these studies primarily focus on field-scale hydraulic fracturing in reservoirs and lack high-resolution validation at the scale of moderate- to small-magnitude seismicity. This limitation hinders accurate assessment of fracture-induced seismic criticality. Applying DAS for simultaneous AE and strain monitoring in laboratory-scale hydraulic fracturing experiments provides a promising approach to reduce uncertainties in fracture parameter estimation, thereby improving fracture network characterization and enabling real-time optimization of fracturing strategies. In the study, we achieved joint monitoring of AEs and fracture attributes using different frequency bands of DAS data, and link the 3D distribution of AEs and relative strain as a basis for determining the orientation of fracture initiation. As a new geophysical monitoring tool, the wide-band response of DAS will be helpful for the identification and monitoring of fractures and AE activities.