Assessment of a deep learning framework for time-lapse seismic monitoring

In the context of CO₂ storage, cost-effective monitor methods are essential to ensure safe and long-term storage. This work explores the use of seismic time-lapse monitoring, combined with deep learning (DL) techniques, to assess potential leakage and migration pathways. The goal is to develop a cost-effective monitoring method while guaranteeing the safety of storage operations. To this end, we propose a Siamese Neural Network (SNN)-based framework to analyse shot gathers, designed to detect and localize changes within the storage complex. We aim to address the challenges of working with large seismic datasets, enabling the identification of significant events with high confidence, while avoiding the need for event-by-event processing. This framework can allow experts to rely on semi-automatic detections while ensuring human evaluation for interpreting and validating the results.

The proposed SNN architecture processes pairs of shot gather from baseline and monitor surveys in a cross-well configuration. It uses two identical neural networks with shared weights to encode the shot gathers into latent feature embeddings, which are then compared to identify similarities and detect changes. By transforming the data into a shared latent space, the model focuses on capturing relevant patterns while filtering out irrelevant variations, ensuring robust and accurate comparisons. When the SNN detects changes between the baseline and the monitor surveys, it highlights the regions where these changes occur. This approach is particularly effective for identifying subtle but important changes in seismic data, such as those caused by CO₂ migration, which alters the velocity and density of the subsurface. Even in noisy data, the SNN can detect these variations, thanks to its ability to learn features that are highly sensitive to small but meaningful changes. The SNN architecture is scalable and can be adaptable to various seismic monitoring tasks, requiring minimal preprocessing. The proposed framework harnesses the power of deep learning to provide insights into the dynamics of the storage complex, with a focus on identifying changes in time-lapse seismic data related to localized variations. The proposed migration detection tool offers a cost-effective and reliable solution to the modern challenges of gas storage monitoring. This study aims to enable operators to identify and address problems promptly, thereby minimising the impact of potential leakages.