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Lithofacies Classification of Thin Layered Reservoirs Through the Integration of Core Data and Dielectric Dispersion Log Measurements

机译:通过核心数据的集成和介电色散日志测量来分类薄层储层的岩石缩放

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Distal turbidites consist of thin laminations(inch-scale)usually ranging from fine sand to clay-rich deposits and may represent major hydrocarbon reservoirs:conventionally,they are studied by means of a log-based binary modeling that discriminates productive and non-productive layers.Nevertheless,the binary model represents a major drawback when dealing with laminations in the silt grain-size range,as their allotment to either end-member can be extremely problematic. This paper deals with a novel lithological facies classification approach that integrates core data and a high-resolution dielectric dispersion wireline log:its 1-inch vertical resolution and a related fit-for-purpose petrophysical model make the log tool’s response suitable to describe the lithological heteroge-neity of these reservoirs. The approach is presented by means of a study performed on the cored section of a well drilled into a laminated gas-bearing Pleistocene reservoir in the Adriatic Basin.A core-based classification was first carried out using sedimentological descriptions,mineralogical analyses,cation exchange capacity mea-surements,routine and special core analyses,and a statistical investigation of grain-size distributions:this allowed the identification of four litho-facies ranging from hemipelagite to coarse silt.Next,a log-based classification was carried out with a multivariate statistical numerical technique(cluster analysis)run on the dielectric dispersion model output curves along the cored section of the well.In the end,a four-facies log-based classification was obtained that matches the core-based classification with an overall agreement in excess of 93%. When compared to the conventional methodology,the presented approach shows the added value of identifying intermediate lithologies,thus leading to a more accurate quantification of the thickness of the potentially hydrocarbon-bearing net reservoir.
机译:远端浊度由薄叠层(英寸级)组成,通常从细砂到富含粘土的沉积物,并且可以代表主要的碳氢化合物储存器:传统上,通过基于日志的二元建模研究它们,可辨别生产性和非生产层。无论如何,二元模型代表在淤泥粒度范围内的叠片时代表一个主要缺点,因为它们对任一端构件的分配可能是非常有问题的。本文涉及一种新型岩性相片分类方法,集成了核心数据和高分辨率介电色散有线日志:其1英寸垂直分辨率和相关的型粪便物理模型使日志工具适合描述岩性的响应这些水库的异性 - 神话。该方法通过对钻孔钻孔井的芯片的研究表达,该研究进入亚得里亚类盆地中的层压含水储层。首先使用沉积物描述,矿物学分析,阳离子交换能力进行核心分类测量,常规和特殊核心分析以及粒度分布的统计调查:这允许鉴定从六管子到粗Silt.next的四个岩石相,以多变量统计进行基于物流的分类数值技术(集群分析)在井芯片沿井芯片的介质色散模型输出曲线上运行。在最后,获得了一种基于核心的分类与总协议匹配的四个基于物流的分类93%。当与传统方法相比,所提出的方法示出了识别中间岩性的附加值,从而导致含有碳氢化合物网储存器的厚度更准确地定量。

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