Microseismic monitoring is an important technique for understanding the behavior of subsurface structures and assessing the risks associated with human activities that can induce seismic events. However, it is also a challenging problem due to the low-energy radiation of the microseismic events, which can make them difficult to detect and locate accurately. In recent years, several innovations in technology and data analysis have addressed the limitations faced in the past.
One major innovation in microseismic monitoring has been utilizing the development of distributed acoustic sensing (DAS) technology. DAS uses fiber-optic cables to detect and measure tiny subsurface vibrations caused by microseismic events. DAS technology has several advantages over traditional seismometers, including a higher frequency spectrum, greater spatial resolution, and the ability to monitor larger areas with fewer sensors.
In addition to technological innovations, there have also been developments in the use of microseismic monitoring for geomechanical modeling and reservoir simulation. By combining microseismic monitoring data with other geophysical and geologic data, such as well logs and seismic surveys, engineers can create detailed models of subsurface structures and behavior. These models can be used to optimize production and reduce the risk of induced seismicity by simulating different scenarios and evaluating their impact on the subsurface.
Despite these innovations, microseismic monitoring remains a challenge due to the complexity of the subsurface and the variability of microseismic events. Microseismic events can be influenced by factors such as stress changes, fluid flow, and rock properties, which can make them difficult to predict and understand. In addition, the small size and low energy of microseismic events can make them difficult to detect and locate accurately, especially in noisy or heterogeneous subsurface environments. To address these challenges, researchers are exploring new techniques for data acquisition and processing, such as passive seismic imaging and joint inversion of multiple data types. Passive seismic imaging uses ambient seismic noise to create high-resolution images of the subsurface, while joint inversion combines data from multiple sources, such as seismic, electromagnetic, and geodetic data, to create more accurate and comprehensive models of subsurface behavior.
To lead off this special section, Altowairqi et al. present recent advances in hydraulic fracture characterization that leverage both borehole-based fiber-optic-acquired microseismic data and strain data, perhaps helping bridge the gap between stimulated reservoir volume and estimated stimulated volume.
Another innovation in microseismic monitoring has been the development of machine learning algorithms for data analysis. Machine learning can be used to analyze large volumes of data from multiple sensors and identify patterns and correlations that may be difficult for humans to detect. For example, machine learning algorithms can be used to identify microseismic events that are similar in location, magnitude, and frequency, which can help improve the accuracy of event detection and location. Machine learning can also be used to predict the likelihood of future microseismic events based on historical data and other factors. Such predictions can help operators optimize production and manage risk.
Boitz and Shapiro present how convolutional neural networks enable DAS-acquired microseismic event detection, while Mizuno and Le Calvez discuss a new DAS-based real-time processing workflow that combines image-based machine learning and traditional processing approaches updated for DAS-acquired data.
Microseismic monitoring has wide-ranging applications in industries such as oil and gas, mining, geothermal energy, and civil engineering. Recent innovations in technology and data analysis have improved the accuracy and resolution of microseismic monitoring, but there is still much to learn about the behavior of subsurface materials and the factors that influence microseismic events. Ongoing research in microseismic monitoring is focused on developing new techniques for data acquisition and processing, as well as improving our understanding of the subsurface through geomechanical modeling and reservoir simulation.