Physics-Constrained LSTM-UNet for Seismic Porosity Prediction in Sandstone Reservoirs

Porosity is a key parameter controlling reservoir storage capacity and fluid flow behavior, and its accurate prediction remains a major challenge in complex reservoirs with limited well control. Traditional methods based on well logs and rock physics modeling are often restricted by sparse spatial coverage, while purely data-driven approaches lack physical interpretability and geological consistency. To address these limitations, this study proposes a model–data coupled framework for seismic porosity prediction in sandstone reservoirs. The approach integrates rock physics constraints into a deep learning architecture by embedding relationships derived from the Gassmann equation and critical porosity model into an LSTM-UNet network. This design enables the model to simultaneously capture spatial features and temporal dependencies from seismic data while maintaining physical consistency. Synthetic experiments based on the SEAM model demonstrate that the proposed method achieves stable and accurate porosity predictions under both noise-free and noisy conditions. Application to field data from a real-world study area further demonstrates the effectiveness of the proposed method, with predicted porosity showing strong agreement with well-log data and improved lateral continuity relative to conventional approaches. The results indicate that the proposed framework effectively combines the flexibility of data-driven learning with the interpretability of physics-based modeling, providing a robust and reliable solution for porosity prediction in complex sandstone reservoirs.