摘要
针对低电阻率油层的电阻率低于或接近邻近水层的电阻率,给测井评价带来很大难度的问题,对沙埝油田沙19断块E1f13油层岩性、物性、粘土矿物、重矿物、地层水矿化度、油层厚度、测井及试油等资料进行了综合分析,研究结果表明,该油田确实存在低电阻率油层,其成因主要包括:岩性细、高地层水矿化度和砂泥岩薄互层。利用BP神经网络,选取最能反映本区低电阻率油层特征的5条测井曲线作为解释的输入参数,建立了比较系统的学习样本,对储层进行预测识别,取得了满意的效果。
It's very difficult to distinguish hydrocarbon-bearing reservoirs from water bearing reservoirs electrically because there is the similar resistivity between them.The analysis on lithology,physical property,clay minerals,heavy minerals,salinity of formation water,thickness of reservoirs,well logs and well testing for E1f13 reservoirs in Sha 19 Fault Block of Shanian Oilfield shows that there are low resistivity reservoirs there,and fine grain lithology,high salinity of formation water,thin interbedding sandstone and mudstone are dominant factors resulting in low resistivity reservoirs in Shanian Oilfield.With BP artificial neural network,five logs which can reflect the property of low resistivity reservoirs is taken as input parameters to establish systematical learning samples and forecast unknown reservoirs.This method is successively applied to identify low resistivity reservoirs in Shanian oilfield.
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第3期49-52,共4页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
国家自然科学基金(4997203)
关键词
沙埝油田
低电阻率油层
成因
识别
BP神经网络
苏北盆地
Shanian Oilfield
low resistivity reservoirs
factors
identification
BP artificial neural network
Subei Basin
