With the ongoing rise in global energy demand, the importance of enhanced oil recovery in oilfield development is becoming increasingly prominent. However, traditional chemical flooding agents face bottlenecks such as poor adaptability to application environments, unclear coupling mechanisms regarding multiple factors, as well as long research and development cycles. This paper systematically discusses the innovative paradigm of oilfield chemical agent development driven by artificial intelligence and proposes four core technological breakthroughs. Firstly, artificial intelligence-empowered molecular simulation technology can reveal the behavior mechanisms of flooding agents under extreme conditions. Secondly, intelligent molecular design using generative adversarial networks and reinforcement learning breaks through the traditional trial-and-error model. Thirdly, the construction of a data-mechanism dual-driven multi-objective optimization model achieves the collaborative prediction of physicochemical properties, economic benefits and environmental friendliness. Lastly, an integrated system of robotic chemist and high-throughput experimental platforms forms a closed-loop system of “artificial intelligence design - automated synthesis - online detection”, yielding a complete ecosystem. The analysis of the current technological development challenges and future development directions reveals that the artificial intelligence-empowered intelligent Research and Development system is expected to promote the transformation of chemical flooding technology toward efficiency, environmental protection and sustainable development, providing a new standard for intelligent oil and gas field development.

Three-phase fluid flow in reservoirs is present in the entire process of oil field development, and three-phase relative permeability data are crucial for reservoir engineering and numerical simulation. At the same time, carbon dioxide flooding and storage have garnered significant attention recently. The calculation of dynamic storage volumes and an in-depth understanding of three-phase flow within formations are inextricably linked to three-phase relative permeability. This review is centered around the available experimental measurements, theoretical models that predict three-phase relative permeability using two-phase data, and four Lattice Boltzmann method models. By analyzing the strengths, weaknesses and limitations of each method and assessing the impact of factors like saturation history, interfacial tension, rock properties, and fluid characteristics on three-phase relative permeability, this paper seeks to offer a comprehensive understanding of the topic. In summary, we provide a concise overview of the prospects and challenges in advancing three-phase relative permeability, serving as a valuable reference for the field of carbon dioxide flooding and storage.