CAJ | 학술논문

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。
탑하유전오도계유장저집체류형적다양성화복잡성，급상규측정정량해석대래료흔대곤난。차류유장저집체류형적화분화식별대유전개발겁위중요。근거6구관건정적전성상측정（FMI）화암심양품특정，장탑하탄산염암저집체화분위미충전동혈형、과탑충전동혈형、궤계충전동혈형화렬봉형4류。의거기중5구정적상규측정향응화4류저집체화분결과，선용 GR、RD、RS、K3（심천측향전조솔도수차적절대치）、AC、DEN 화 CNL 공7충측정신식작위신경망락적수입학습양본，경학습훈련건립료저층화분적신경망락모형。통과검험정대비，설명기우신경망락모형，통과상규측정적다삼수약속，가이판단암용형저집체류형。경본차연구，초보형성료저집체반정량평개표준。
tion 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.