Automated Microseismic Event Detection for Downhole Distributed Acoustic Sensing Data Processing
Authors: Liu Yongzan , Liang Lin , Podgornova Olga , Zeroug Smaine , Mizuno Takashi , Le Calvez Joel 
Published: September 2023

ABSTRACT
Distributed acoustic sensing (DAS) can provide high-resolution measurements owing to the closely spaced sensing channels over a long distance and high sampling rate – attributes that are beneficial to seismic monitoring and analysis. However, DAS arrays generally suffer from a lower signal-to-noise ratio (SNR). Additionally, dense spatial and temporal measurements can result in extremely large data volumes that hinder efficient data storage, transmission, and processing. Automatically and accurately detecting seismic events in continuous DAS data is challenging. In this study, we present a microseismic event detection workflow that is based on coherency analysis of the waveforms and accommodates the high-resolution characteristics of DAS data. DAS data is transformed into an apparent velocity-time (v-t) domain by slant-stacking the data along different apparent velocities, i.e., slopes of the waveform, at each time sample. The coherency of the slant-stacked waveform is measured by the semblance coefficient. A coherent signal is identified if the semblance coefficient is larger than a threshold that is adaptively calculated. Then, the density-based spatial clustering (DBSCAN) algorithm is applied to group the coherent detections into different clusters. Finally, the microseismic events are detected by filtering out the clusters of coherent noise. The algorithm generates low-volume time-windowed data containing microseismic events that can be efficiently transferred and processed to estimate event location, magnitude, and source mechanism. The performance of the developed algorithm is tested using two field datasets from the Utah FORGE project. The results demonstrate the potential of the algorithm to achieve automatic real-time microseismic event detection for downhole DAS systems.

INTRODUCTION
Microseismic monitoring is a critical element for a variety of subsurface operations such as hydraulic fracturing in unconventional reservoirs, water circulation in Enhanced Geothermal Systems (EGS), as well as carbon injection and storage in geological reservoirs. In hydraulic fracturing treatments, microseismicity is important for mapping and characterizing created fracture networks. In fluid injection and storage operations, potential seismic hazard may occur due to the large volume of injected fluid. Seismic monitoring, being also a regulatory requirement, offers an early warning tool to avoid and mitigate seismic hazard, also known as traffic light monitoring system. Seismic event detection is the first step for subsequent processing encompassing event location, magnitude estimation, and source mechanism characterization. Recently, distributed acoustic sensing (DAS) has been increasingly used for microseismic monitoring. DAS makes use of Rayleigh scattering of laser pulses in fiber-optic cables to measure strain rate along a fiber (Hartog, 2017). One significant advantage of DAS is the high spatial-temporal resolution and large coverage. The fiber can be kilometers long and the channel spacing is in the order of meters, such that thousands of measurements can be obtained at each time sample. The sampling rate of DAS can be thousands of Hertz (Hz). Compared with the traditional geophone or accelerometer-type sensors, where there are only tens of sensing points with large inter-sensor spacing, DAS can provide a more complete picture of microseismic waveforms propagating in time and space. However, the dense spatial and temporal measurements generate extremely large data volumes. Furthermore, the signal-to-noise ratio of DAS data is generally lower than that of the geophone-acquired data. These challenges require the development of new methods for automated real-time microseismic monitoring and analysis.