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.

The rapid integration of artificial intelligence into oil and gas exploration and development offers transformative opportunities within the context of the global energy transition. This article highlights the key advancements and challenges in artificial intelligence applications. Machine learning algorithms enable data-driven shale sweet spot prediction, overcoming the limitations of traditional methods by capturing complex controlling factors. Intelligent core image analysis, leveraging computer vision and foundation models, enables automatic mineral identification, pore analysis, and rock structure characterization, thereby providing a comprehensive framework for microscopic reservoir appraisal. Physics-informed neural networks address the limitations of purely data-driven reservoir simulation by embedding governing seepage equations into their loss functions, thereby ensuring physical consistency and improved generalization. Multimodal architectures significantly enhance unconventional shale gas production prediction by integrating geological heterogeneity with dynamic production behavior, leading to more accurate and stable forecasts. Collectively, these AI-driven approaches underscore the importance of combining domain expertise, multi-source data, and physics-aware modeling to achieve efficient and intelligent oil and gas development.