DeepNRMS: Unsupervised Deep Learning for Noise-Robust CO2 Monitoring in Time-Lapse Seismic Images

Abstract

Monitoring stored CO2 in carbon capture and storage projects is crucial for ensuring safety and effectiveness. We introduce DeepNRMS, a novel noise-robust method that effectively handles time-lapse noise in seismic images. The DeepNRMS leverages unsupervised deep learning to acquire knowledge of time-lapse noise characteristics from pre-injection surveys. By utilizing this learned knowledge, our approach accurately discerns CO2-induced subtle signals from the high-amplitude time-lapse noise, ensuring fidelity in monitoring while reducing costs by enabling sparse acquisition. We evaluate our method using synthetic data and field data acquired in the Aquistore project. In the synthetic experiments, we simulate time-lapse noise by incorporating random near-surface effects in the elastic properties of the subsurface model. We train our neural networks exclusively on pre-injection seismic images and subsequently predict CO2 locations from post-injection seismic images. In the field data analysis from Aquistore, the images from pre-injection surveys are utilized to train the neural networks with the characteristics of time-lapse noise, followed by identifying CO2 plumes within two post-injection surveys. The outcomes demonstrate the improved accuracy achieved by the DeepNRMS, effectively addressing the strong time-lapse noise.