塔河油田缝洞型碳酸盐岩油藏原油储量丰富，受构造及岩溶作用控制，储集体类型多样，非均质性强，不同类型储集体具有各自的开发特征，准确识别储集体类型是后续生产措施制定和油藏有效开发的基本前提。针对塔河油田雅克拉区块潜山碳酸盐岩油气藏储集体类型判别的实际需求提出一套储集体类型动态识别方法，基于塔河油田生产井动态数据分析，在单井开发阶段划分的基础上，提取与储集体类型相关性强的弹性驱初期产油量、弹性驱时间、弹性驱累计产油量、弹性驱产量月递减率4项动态参数作为判别指标，通过聚类分析形成动态参数量化标准，最终结合人工神经网络技术实现基于动态资料的储集体类型自动批量识别，获得的储集体识别结果与钻录井和地球物理资料确定的储集体类型吻合度达80 %以上。该自动识别方法具有参数明确、识别结果准确性高、可操作性强的特点，可辅助地质资料对储集体进行更准确的判定，更可应用于地质资料较少地区的碳酸盐岩油藏储集体研究，为油藏有效开发提供科学依据。

Tahe Oilfield, known for its substantial crude oil reserves, features fracture-vuggy carbonate reservoirs with diverse and heterogeneous characteristics shaped by structural and karstic influences. Each reservoir type within this field exhibits distinct development traits, making the precise identification of these reservoir types crucial for devising effective production strategies and optimizing oil reservoir development. However, the identification of reservoirs through drilling and geophysical data is challenging and costly, hence, this paper focuses on the dynamic identification of the vuggy, fractured-vuggy, and fractured reservoirs in the buried hill carbonate reservoirs in the Yakela block of Tahe Oilfield. The research initially involved analyzing the dynamic data of the production wells in this area and dividing the development stages of each well. Subsequently, the discriminant indicators, such as the initial oil production in the elastic stage, elastic time, cumulative oil production, and production decline rate were extracted. These indicators are generally available in each well and have less human interference. They form the basis of a dynamic quantitative characterization method for determiningreservoir types Through the utilization of mathematical statistics and artificial neural network technology, an automatic identification system for carbonate reservoir types based on dynamic data was established. Remarkably, the results obtained from this method align with over 80 % of the reservoir types determined through drilling logging and geophysical data. This automated identification method proves to be highly operable and complements geological data effectively, enabling more precise reservoir determination, especially in areas where geological information is scarce. Its applicability extends to carbonate reservoir research in regions with limited data, offering reliable reservoir-type results that are essential for informed development planning.