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.
Zhao et al. (Mon,) studied this question.