Machine learning-based time-lapse 1D seismic full-waveform inversion with efficient training data generation in a carbon capture and storage monitoring

Abstract

Machine learning (ML)-based time-lapse and seismic full-waveform inversion has recently gained attention in carbon capture and storage (CCS) research because of its potential to overcome the limitations of theory-based inversion and its excellent imaging performance for CO2 plumes. We propose a ML-based time-lapse 1D inversion method with efficient training data generation. The proposed method exploits time-lapse seismic data to infer time-lapse velocity changes and uses a 1D inversion strategy to enable the UNet3+ model to learn the general characteristics of velocity changes between baseline and monitoring phases. Our proposed method quickly generates various 1D velocity models and rapidly provides numerous near-offset data by using 1D ray-based modeling method. This allows us to overcome a challenge in constructing of 2D velocity models based on limited geological assumptions and forward modeling to generate seismic gathers, which requires massive computational resources. However, because 2D effects such as diffraction caused by the CO2 plume only affect the monitoring data, inaccurate predictions in the 1D inversion occurred near the plume, following the diffraction trajectory. We corrected this effect using a diffraction removal process for 2D target data, thereby improving the prediction accuracy of the ML model. The ML model was trained with a large amount of training data generated using the proposed method and validated on 2D target data with three different CO2 injection plume shapes obtained from a modified Marmousi2 velocity model. As a result, information about the CO2 plume was reasonably delineated, although the magnitude of the time-lapse velocity change near the plume boundary was reduced by small amplitude losses during the diffraction removal process. Our results suggest that the proposed method is a powerful tool for CCS monitoring, which requires forward modeling for each iterative survey.