Dynamic Reservoir Property Estimation With Uncertainty Quantification From 4D Seismic Data Using Bayesian Neural Networks

Quantifying dynamic reservoir properties, such as pressure and saturation from 4D seismic data, is crucial for improving reservoir management, increasing hydrocarbon recovery and maximizing economic returns. Although data-driven approaches, particularly deep neural networks (DNNs), have shown promise in mapping seismic attributes to reservoir properties, they are often deterministic and lack robust uncertainty quantification. This limitation is critical in geosciences, where decisions must be made despite inherent data noise and model limitations. We propose a Bayesian Neural Network (BNN) framework to comprehensively quantify both aleatoric (data-related) and epistemic (model-related) uncertainty in 4D seismic inversion. Our method extends a deterministic autoencoder architecture into a probabilistic one by treating network weights as distributions. Furthermore, we leverage data from multiple repeated 4D seismic surveys, rather than a single baseline-monitor pair, to capture spatio-temporal trends. Synthetic validation demonstrates the model's ability to provide well-calibrated uncertainty estimates. Application to the Schiehallion field shows that the BNN successfully estimates pressure and saturation changes, with uncertainty maps highlighting areas affected by noise, data sparsity or competing geophysical effects. The results provide reservoir engineers with critical confidence intervals for dynamic property estimates, enabling more informed and risk-aware decision-making.