Accurate geological modeling of shallow-water delta reservoirs remains challenging due to complex sedimentary architecture and strong heterogeneity. This study develops an advanced modeling technique that integrates geological process understanding with deep learning, with a focus on the accurate representation of channel geometry under multiple data constraints. Field outcrop investigations of the Chang 6 Member in the Ordos Basin were conducted to clarify key geological characteristics and geometric parameters of shallow-water distributary channels. An improved object-based method was employed to effectively generate three-dimensional training datasets capturing typical channel bifurcation and convergence patterns. A conditional progressive generative adversarial network is proposed to incorporate multi-source constraints, including global geological features, well logs, and seismic probability volumes, thereby enabling simultaneous learning of geological patterns and data fidelity. Application to a shallow-water delta reservoir in the Ordos Basin demonstrates that the method produces geologically realistic facies models that honor all available constraints, significantly improving modeling accuracy and computational efficiency. This work provides an innovative and adaptive methodology for intelligent modeling of complex reservoir systems.