Clustering Analysis for Improved Characterization of Carbonate Reservoirs in a Southern Iraqi Oil Field

Abstract

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.