摘要
由于常规小数控声波-感应测井系列纵向分辨率低,对薄砂体的油层识别存在误判和漏判。应用3种不同的研究方法综合识别薄砂体油层:首先,从沉积微相精细研究入手,分析在不同沉积微相下储层物性与电性响应特征之间的关系,建立基于沉积微相精细研究基础上的图形交会识别;其次,在油藏特征分析的基础上,明确非统一油水界面层状构造油藏界面之上只有渗透层(油层)和非渗透层(干层)的区别,建立微电极幅度差薄层识别;最后,采用BP人工神经网络评价技术,提取8种反映油层特征的测井曲线及计算参数,通过学习分析,输出结果与测井精细解释相互验证。该技术在高集油田的应用,大大提高了对薄砂体油层的解释精度。
Due to the low longitudinal resolution of conventional and small CNC acoustic-induction logging series,there existed an error and missing judgement of thin sandbody reservoirs.Different methods were used to identify the thinsand reservoirs,first,the cross-plot identification based on microfacies was built by studying the sedimentary microfacies and analysis of the relationship between reservoir properties and electrical response.Second,based on analysis of oil reservoir,the difference was clarified between permeable reservoir(oil layer)and non-permeable reservoir(dry layer)on the interface of stratified structural reservoirs of non-uniform oil and gas interface,the microelectrode separation thin section identification was established.Third,BP neural network technology was adopted in this area,the parameters from 8 kinds of characteristic logging curve were obtained.The output results are compared with that of the fine interpretation method and checked by each other.This technology is applied in Gaoji Oilfield,and it greatly improves the interpretation accuracy thin sand-body reservoirs.
出处
《石油天然气学报》
CAS
CSCD
2014年第12期117-121,8,共5页
Journal of Oil and Gas Technology
基金
中石化江苏油田分公司项目"金湖凹陷非主力层油气层识别技术及增储潜力研究"(JS11001)产出论文
关键词
薄砂体油层
测井精细解释
沉积微相
微电极
神经网络
thin sand-body reservoir
fine logging interpretation
sedimentary microfacies
micro electrode
neural network
