聚类分析改进伊拉克南部油田碳酸盐岩储层特征

W. Al-Mudhafar, Erfan M. Al lawe, C. I. Noshi
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引用次数: 11

摘要

相预测是储层表征和建模的重要步骤。确定具有代表性的储层模型,将提高储层开发的进程,优化经济策略。缺乏数据是储层描述的一个关键问题,因此必须采用替代方法来改进储层描述过程。在本研究中,利用伊拉克南部某油藏的测井和岩心数据,将聚类分析作为一种统计方法来对储层相进行分类。本研究采用了非均质碳酸盐岩储层的数据。数据包括测井记录,如;GR、SP、密度、中子孔隙度、总孔隙度、电阻率、磁感应、页岩体积、含水饱和度,以及岩心分析的孔隙度和渗透率值。通过统计工具对这些数据进行整合和分析,进行聚类分析。聚类分析是一种寻找特定群体或点之间的异同点,从而将其划分为不同类别的方法。这个概念是通过使用R软件实现的,R软件是一个非常强大的开源工具,用于统计研究,具有各种功能和包。基于给定数据,采用K-mean方法和Calinski-Harabasz方法两种不同的聚类算法对储层相进行分类。研究结果表明,在给定测井记录的情况下,通过不同的聚类分析可以预测储层相。K-means方法预测了5种最佳相类型,而Calinski-Harabasz技术预测了3种最佳储层相类型。两种聚类分析方法在相分类上的差异是由于两种方法的敏感性,因为该井所有单元的岩石质量都很高,这使得识别相具有挑战性,因为所有层都具有相似的储层性质。
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Clustering Analysis for Improved Characterization of Carbonate Reservoirs in a Southern Iraqi Oil Field
Facies prediction is an important step in reservoir characterization and modeling. Define a representative reservoir model will enhance the process of reservoir development and will optimize the economic strategies. The lack of data is a key issue in reservoir characterizations and therefore alternative approaches have to be adopted to improve the process of reservoir characterization. In this research, clustering analysis was implemented as a statistical solution to classify reservoir facies given well logs and core data in a reservoir from the south of Iraq. In this research, data from a heterogeneous carbonate reservoir were used. The data included well log records such; GR, SP, Density, Neutron Porosity, Total Porosity, Resistivity, Induction, Shale Volume, Water Saturation, along with porosity and permeability values from core analysis. These data were integrated and analyzed through statistical tools to perform clustering analysis. The clustering analysis is an approach of finding the similarities and differences between specific groups or points in order to classify them into different classes. This concept was implemented by the use of R software, which is a quite powerful open source tool for statistical studies with variety of functions and packages. Two different clustering algorithms, K-mean approach and Calinski-Harabasz solution were used to classify reservoir facies based on the given data. The results of this research show that the reservoir facies can be predicted through different clustering analysis when well logs records are given. K-means approach has predicted the optimal facies classification to be five, while Calinski-Harabasz technique has identified three optimal reservoir facies. The difference in facies classification between the two clustering analysis approaches is attributed to the two approaches sensitivity because of the high quality rocks in all the units of this well, which makes it challenging to identify the facies as all the layers have similer reservoir properties.
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