DeepNRMS: Unsupervised deep learning for noise-robust CO2 monitoring in time-lapse seismic images

Abstract

Monitoring stored ${\mathrm{CO}}_2$ 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 preinjection surveys. By using this learned knowledge, our approach accurately discerns ${\mathrm{CO}}_2$-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 preinjection seismic images and subsequently predict ${\mathrm{CO}}_2$ locations from postinjection seismic images. In the field data analysis from Aquistore, the images from preinjection surveys are used to train the neural networks with the characteristics of time-lapse noise, followed by identifying ${\mathrm{CO}}_2$ plumes within two postinjection surveys. The outcomes demonstrate the improved accuracy achieved by DeepNRMS, effectively addressing the strong time-lapse noise.