石油与天然气工程智能化已成为行业发展的必然趋势，其中智能钻完井技术可以大幅提高钻井效率和钻遇率，降低施工成本，被视为油气领域的一项变革性技术和前沿热点。机理-数据融合的智能建模、数字孪生等人工智能方法及其在油气钻完井工程领域的应用已取得广泛关注和关键进展，但是智能钻完井技术研究仍然处于初级阶段。在人工智能、大数据等前沿技术与油气钻完井工程深度融合的过程中，智能钻完井场景体系、多源多尺度数据治理、机理-数据混合驱动、模型可解释性、模型迁移性和不确定性建模等面临诸多挑战。为此，本文系统提出了钻完井人工智能应用场景体系，全面阐述了各场景下的智能技术及研究进展，深入探讨了智能钻完井技术未来发展的重点方向，为人工智能技术落地油气钻完井工程提供参考。

The application of artificial intelligence (AI) has become inevitable in the petroleum industry. In drilling and completion engineering, AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency (DE). In recent years, numerous studies have focused on intelligent algorithms and their application. Advanced technologies, such as digital twins and physics-guided neural networks, are expected to play roles in drilling and completion engineering. However, many challenges remain to be addressed, such as the automatic processing of multi-source and multi-scale data. Additionally, in intelligent drilling and completion, methods for the fusion of data-driven and physicsbased models, few-sample learning, uncertainty modeling, and the interpretability and transferability of intelligent algorithms are research frontiers. Based on intelligent application scenarios, this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future. This study aims to enhance the berthing of AI techniques in drilling and completion engineering.