Gas leakage recognition for CO₂ geological sequestration based on the time series neural network

The leakage of stored and transported CO₂ is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO₂ leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO₂ variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO₂ data was applied to predict the daily atmospheric CO₂. Further, the recurrent nonlinear autoregressive with exogenous input (NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO₂ storage and transportation technologies.