Analysis of time-series monitoring data for early detection of CO2 leakage to shallow subsurface environments

Abstract

Time-series data of environmental parameters have been gathered in numerous artificial CO2 release experiments performed globally; however, their usability must be re-evaluated because they are generally utilized as auxiliary data, such as model construction or system operation checks. In this study, we propose a process for determining the environmental changepoint (EnCP) using continuous data for the reliable fast detection of unexpected CO2 intrusion into a shallow subsurface. From a controlled CO2 gas release test, continuous data on electrical conductivity, pH, and partial pressure of CO2 (pCO2) in the saturated zone, as well as CO2 concentration in the vadose zone were obtained and analyzed using four methods (simple excess, simple moving average, offline, and online change-point detection) to determine the environmental change-point caused by CO2 appearance. We found that changes caused by CO2 gas injection occurred rapidly, allowing the EnCP detected in the time-series analysis to identify changes more efficiently than the occasional sampling, which was performed at least once a day. It was confirmed that pCO2 had the highest sensitivity among the saturated zone indicators, and the online change-point method was more successful than other methods in determining EnCP effectively. Furthermore, the determined EnCPs at each monitoring point, showing the front boundary of the injected CO2, allowed for the comprehensive understanding of CO2 behavior in both the saturated and vadose zones. This may be used to aid in the design and operation of a real-time CO2 leakage surveillance network system for the early detection of CO2 intrusion into shallow subsurface environments.