Fracture–vuggy carbonate reservoirs exhibit strong heterogeneity, spatial discontinuity, and highly variable porosity, which limit the effectiveness of traditional seismic attributes and conventional inversion. Focusing on the XX well block in the Tarim Basin, this study develops a rock-physics-constrained Physics-Constrained TransUNet method for intelligent porosity prediction. A carbonate-specific rock-physics model is first established, considering mineral composition, pore type, and water saturation, ensuring physical consistency between porosity, elastic parameters, and seismic responses. On this basis, a deep-learning framework integrating U-Net multi-scale feature extraction and Transformer global modeling is constructed. By embedding rock-physics priors, regularization constraints, and log-derived porosity labels, the method forms a unified physics- and data-driven inversion scheme. Applications to multiple deep wells and 3D post-stack seismic data from the FI7 fault zone demonstrate stable training, rapid convergence, and strong capability in capturing nonlinear porosity–seismic relationships. Compared with conventional inversion, the proposed approach significantly improves prediction accuracy in cavern-dominated intervals, fractured zones, and areas with abrupt porosity changes, while maintaining robust lateral continuity. Inter-well sections and target-layer slices further verify its effectiveness in identifying fracture–dissolution–vug composite reservoirs. The method provides a practical and reliable workflow for porosity prediction in ultra-deep carbonate reservoirs, supporting fine reservoir characterization and sweet-spot evaluation.

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.