摘要： 储层保护在油气勘探开发全过程中具有重要的战略意义,深层油气资源开发环境复杂、技术要求高,完善的储层保护技术能够保障油气勘探开发过程实现"少投入、多产出、显著提高经济效益"的目的,应该重视其在钻井、完井、采油等各个阶段中的关键核心作用.近年来,机器学习等人工智能技术得到广泛应用,为储层保护提供了智能化的解决方案,智能化储层保护技术成为大势所趋.通过系统归纳近年来储层保护与人工智能技术融合的相关文献,分析了储层保护中所使用的模型方法、敏感性损害预测数据集特征、智能决策系统开发及应用的研究现状.总结"人工智能+储层保护"技术的落地局限性,主要表现在:第一,数据质量参差不齐,导致模型的输入基础不够可靠;第二,应用场景复杂,各类油气田的工程环境差异大,模型在应对复杂情况时效果不一;第三,模型泛化能力较低,在不同场景中的适用性受到局限;第四,配套软件研发体系不够成熟,制约了软件的落地应用.针对现有问题,提出未来发展的重点方向:首先,需要进行数据治理以提升数据质量,构建高质量的储层保护数据库;其次,应该在智能模型中融入储层保护领域的专业知识,提高模型精度;第三,需要增强模型的可解释性,提升技术人员对模型预测结果的信任度;最后,开发基于大模型环境的智能决策支持系统,实现更高层次的智能化储层保护方案.

关键词: 储层保护, 人工智能, 损害预测, 储层敏感性, 智能决策系统

Abstract: 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"low input,high output,and significantly improved eco-nomic 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 sys-tematic 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 generaliz-ability,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.

Key words: reservoir protection, artificial intelligence, damage prediction, reservoir sensitivity, intelligent decision system

中图分类号: 

• TE254 TP181