Micro-Seismic Monitoring Data Analysis System Based on Sequentially Discounting Autoregressive and its Application to Offshore CO2 Storage Safety Operation

Abstract

Micro-seismic monitoring is widely used as an effective way in detecting the seismic events during injection to ensure the safety during the injection of CO2 into geologic formations and is beneficial for increasing the public acceptance. At the offshore site, the challenges in monitoring involve processing high noise record and distinguishing the induced seismic events from the background natural earthquakes. We introduce the Sequentially Discounting AutoRegressive (SDAR) learning method to identify seismic events against the background noise. The SDAR method has a higher detection rate than traditional methods (e.g., STA/LTA), even for low signal to noise (< 1.0) events. We apply the SDAR method to synthetic record and the Tomakomai Ocean Bottom Cable (OBC) baseline data to test the method effectiveness. The detected background natural earthquakes will then be used to construct the seismic model and identify the induced seismic events during the injection. Based on the SDAR method, an Advanced Traffic Light System (ATLS) is being built for induced seismicity monitoring and injection safety management at the Tomakomai storage project. Our results suggest that the SDAR-based ATLS will be a promising tool for safety management, especially for injection projects in offshore storage project.