Spatial-Temporal Densely Connected Convolutional Networks: An Application to CO2 Leakage Detection

TL;DR

This paper introduces a novel data-driven, densely connected convolutional network architecture that effectively detects and quantifies CO2 leakage from seismic data, enhancing monitoring accuracy in carbon sequestration.

Contribution

The paper presents a new spatial-temporal densely connected CNN architecture combining 1-D and 2-D convolutions for CO2 leakage detection, reducing computational cost and improving accuracy.

Findings

Accurately detects CO2 leakage mass from seismic data

Reduces network parameters through dense connectivity

Demonstrates effectiveness on synthetic Kimberlina dataset

Abstract

In carbon capture and sequestration, building an effective monitoring method is a crucial step to detect and respond to CO2 leakage. CO2 leakage detection methods rely on geophysical observations and monitoring sensor network. However, traditional methods usually require physical models to be interpreted by experts, and the accuracy of these methods will be restricted by different application conditions. In this paper, we develop a novel data-driven detection method based on densely connected convolutional networks. Our detection method learns a mapping relation between seismic data and the CO2 leakage mass. To account for the spatial and temporal characteristics of seismic data, we design a novel network architecture by combining 1-D and 2-D convolutional neural networks together. To overcome the expensive computational cost, we further apply a densely-connecting policy to our network architecture to reduce the network parameters. We employ our detection method to synthetic seismic datasets using Kimberlina model. The numerical results show that our leakage detection method accurately detects the leakage mass. Therefore, our novel CO2 leakage detection method has great potential for monitoring CO2 storage.