塔河油田奥陶系碳酸盐岩岩溶储集体类型及划分方法研究

塔河油田奥陶系油藏储集体类型的多样性和复杂性，给常规测井定量解释带来了很大困难。此类油藏储集体类型的划分和识别对油田开发极为重要。根据6口关键井的电成像测井(FMI)和岩心样品特征，将塔河碳酸盐岩储集体划分为未充填洞穴型、垮塌充填洞穴型、机械充填洞穴型和裂缝型4类。依据其中5口井的常规测井响应和4类储集体划分结果，选用GR、RD、RS、K3(深浅侧向电阻率倒数差的绝对值)、AC、DEN和CNL共7种测井信息作为神经网络的输入学习样本，经学习训练建立了储层划分的神经网络模型。通过检验井对比，说明基于神经网络模型，通过常规测井的多参数约束，可以判断岩溶型储集体类型。经本次研究，初步形成了储集体半定量评价标准。

Approach to Karst Reservoir Types and Classification of Ordovician Carbonate in Tahe Oilfield

The Ordovician reservoir of the Tahe oilfield is a complex fracture and vuggy karst carbonate reservoir which experienced multiphase karst and tectonic disruption. This is very difficult to identify the types of karst cave by conventional log data. The main reservoir space include karst cave, vug and cracks, which control distribution and accumulation of the reservoir fluid. It is very important for petroleum development that the different reservoir types are divided and distinguished. In this paper, based on six key wells of electric imaging logging (FMI), and the core sample characteristics, combined with the corresponding conventional logging data, the carbonate reservoir of the Tahe oilfield is divided into four categories：unfilled cave，collapsed cave, cave-filling of sand shale, solution fracture type. According to the conventional well logging response and result of four kinds of reservoir division, seven kinds of logging information GR, RD, RS, K3 (of the absolute value of lateral resistivity difference from bottom), AC, DEN and CNL are taken as inputs of neural network learning samples. By training neural network model of reservoir division, the neural networks model of reservoir type’s identification is established. Based on this network model, take TK604 well as an example to test the classification in the Tahe areas 6 and 7. Real data processing has proved that this method is effective. Through the actual data processing proved that the conventional log multiparameter constraint can be used to judge karst reservoir type, and initially semi-quantitative evaluation standard is formed, showing preliminarily that the method of the neural network method based on the imaging logging is suitable for complex carbonate reservoir in Tahe oilfield.