CO2 storage monitoring based on time-lapse seismic data via deep learning

Abstract

CO2 geological storage is an effective way of reducing atmospheric CO2 concentration. The physical characteristics of the reservoir and the feasibility of CO2 flooding were analyzed based on the carbon capture, utilization, and storage (CCUS) project in Yanchang Oilfield, China. The heterogeneity of the reservoir indicates variation in the shape of the CO2 plume. A CO2 storage monitoring method based on a full convolutional neural network (FCN) that applies time-lapse difference seismic data is proposed for the geological task of CO2 storage monitoring. A total of 2800 sets of forward simulation time-lapse difference data and corresponding velocity changes were used to train the network. These data include noisy and noise-free data, which help improve the noise immunity of the network. The prediction accuracy of the network under different samples of training data, noise levels, and velocity changes is discussed. Furthermore, 100 sets of noisy data and 100 sets of noise-free data were used to verify the predictive ability of the network. The test results of the synthetic data show that the FCN-based CO2 storage monitoring method exhibits relatively high efficiency and high precision compared with time-lapse full waveform inversion.