150 Lithofacies Classification using Borehole Image Log and Multi Resolution Graph based Clustering to reservoir characterization


One of the most important parameters in understanding static modeling of reservoir is having facies and their distribution in the reservoir zone. Since in the most of reservoirs facies distribution directly related to the permeability and porosity variation, therefore having these parameters can gain an estimate of the distribution of reservoir parameters. To determine lithofacies in wells and in reservoir there are different methods. One of the common method is to use the core drilling based on geological study. Recently with the image logs one can measure fracture distribution and it is a powerful tools for studying lithofacies as well. As a new work done one can determine a method to predict facies types and facies variations using image logs. Formation Micro Imager, FMI, log is the tool for illustrating geological markers through wells. FMI log could provide 80% coverage of well by high-resolution data [1]. In this study, South Pars gas field is studied. First, Fullset logs and cross plot are generated, in order to formation evaluation, then the amounts petrophysical parameters such as saturation and effective porosity are estimated for different zones. Section K1, K2 and K4 are the reservoir areas and hydrocarbon contained zones are determined. After Lithofacies classification based on core – fullset log data and processing of FMI datasets. Finally, Lithofacies based on FMI log data modeled and clustered, this model gives satisfactory results when compared to core-log and core observed reservoir facies.

Keywords: FMI log, Classification, Characterization, Lithofacies.


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