Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring

Underground energy storage, which includes storage of hydrogen, compressed air, and CO2, requires careful monitoring to track potential leakage pathways, a situation where time-lapse seismic imaging alone may be inadequate. A recently developed Digital Shadow (DS) enhances forecasting using machine learning and Bayesian inference, yet their accuracy depends on assumed rock physics models, the mismatch of which can lead to unreliable predictions for the reservoir's state (saturation/pressure). Augmenting DS training with multiple rock physics models mitigates errors but averages over uncertainties, obscuring their sources. To address this challenge, we introduce context-aware sensitivity analysis inspired by amortized Bayesian inference, allowing the DS to learn explicit dependencies between seismic data, the reservoir state, e.g., CO2 saturation, and rock physics models. At inference time, this approach allows for real-time ''what if'' scenario testing rather than relying on costly retraining, thereby enhancing interpretability and decision-making for safer, more reliable underground storage.