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Reservoir protection plays a significant strategic role throughout the entire process of oil and gas exploration and development. The development of deep oil and gas resources faces complex environmental conditions and high technical demands. Effective reservoir protection technologies can help achieve the goal of “l(fā)ow input, high output, and significantly improved economic efficiency.” As such, the role of reservoir protection in various stages such as drilling, completion, and production is critical. In recent years, the widespread application of machine learning and other Artificial Intelligence (AI) technologies has provided intelligent solutions for reservoir protection, making smart reservoir protection technologies a major trend in the industry. A systematic review of recent literature on the integration of artificial intelligence and reservoir protection has been carried out to analyze the various model methods, the characteristics of sensitivity damage prediction data sets, and the development and application of intelligent decision systems used in reservoir protection. Through this review, the study identifies several key issues and limitations when applying “AI + reservoir protection” technologies. Firstly, the data quality is inconsistent, leading to unreliable inputs for model training. Secondly, the application scenarios are complex; the engineering environments of different oil and gas fields vary widely, and models may not perform effectively in these complex, heterogeneous conditions. Thirdly, models have low generalizability, and their adaptability in various scenarios is often limited, making it difficult to apply them universally across different field conditions. Finally, the supporting software and development systems for these models are not fully matured, restricting the practical implementation of these intelligent solutions. To address these challenges, several directions for future development are proposed. Firstly, improving data governance to enhance the quality of data is essential. This can be achieved by constructing a high-quality reservoir protection database, which would provide reliable data for training and optimizing intelligent models. Secondly, it is crucial to integrate domain-specific knowledge from the reservoir protection field into intelligent models. Incorporating expert knowledge into the models can improve their accuracy and predictive performance, making them more suitable for real-world applications in reservoir management. Thirdly, model interpretability should be enhanced. Increasing the transparency of decision-making processes within AI models will help build trust among technical personnel in the predicted results, thereby encouraging their acceptance and adoption of these systems. Finally, there is a need for the development of intelligent decision support systems that can handle large models, ultimately facilitating more advanced, high-level smart solutions for reservoir protection.
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