Gas leakage recognition for CO<sub>2</sub> geological sequestration based on the time series neural network

Denglong Ma; Jianmin Gao; Zhiyong Gao; Hongquan Jiang; Zaoxiao Zhang; Juntai Xie

doi:10.1016/j.cjche.2020.06.014

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Gas leakage recognition for CO₂ geological sequestration based on the time series neural network
- Gas leakage recognition for CO₂ geological sequestration based on the time series neural network
- 中国化学工程学报（英文版） 2020年28卷第9期页码：2343-2357
- Affiliations：
  
  1. School of Mechanical Engineering, Xi'an Jiaotong University,Xi'an,China,710049
  2. School of Chemical Engineering and Technology, Xi'an Jiaotong University,Xi'an,China,710049
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2020.06.014
  中图分类号：
- 纸质出版：2020
- Accepted：
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Denglong Ma, Jianmin Gao, Zhiyong Gao, 等. Gas leakage recognition for CO₂ geological sequestration based on the time series neural network[J]. 中国化学工程学报（英文版）, 2020,28(9):2343-2357. DOI: 10.1016/j.cjche.2020.06.014.

geological sequestration based on the time series neural network[J]. 中国化学工程学报, 2020, 28(9): 2343-2357.
Denglong Ma, Jianmin Gao, Zhiyong Gao, 等. Gas leakage recognition for CO₂ geological sequestration based on the time series neural network[J]. 中国化学工程学报（英文版）, 2020,28(9):2343-2357. DOI: 10.1016/j.cjche.2020.06.014. DOI：

geological sequestration based on the time series neural network[J]. 中国化学工程学报, 2020, 28(9): 2343-2357. DOI： 10.1016/j.cjche.2020.06.014.

摘要

Abstract

The leakage of stored and transported CO

is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO

leakage signal in the complex atmosphere background. In this work

a time series model was proposed to forecast the atmospheric CO

variation and the approximation error of the model was utilized to recognize the leakage. First

the fitting neural network trained with recently past CO

data was applied to predict the daily atmospheric CO

. 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 CO

storage and transportation technologies.

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

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

DOI：10.1016/j.cjche.2020.06.014

摘要

Abstract

关键词

Keywords

references

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

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