A Case Study of Primary and Secondary Porosity Effect for Permeability Value in Carbonate Reservoir using Differential Effective Medium and Adaptive Neuro-Fuzzy Inference System Method

Pore system in a carbonate reservoir is very complex compared to the pore system in clastic rocks. According to measurements of the velocity propagation of sonic waves in rocks, there are three types of carbonate pore classifi cations: Interpartikel, Vugs and Crack. Due to the complexity of various pore types, errors in reservoir calculation or interpretation might occur. It was making the characterization of the carbonate reservoir more challenging. Differential Effective Medium (DEM) is an elastic modulus modeling method that considers the heterogeneity of pores in the carbonate reservoir. This method adds pore-type inclusions gradually into the host material to the desired proportion of the material. In this research, elastic modulus modeling will be carried out by taking into account the pore complexity of the carbonate reservoir. ANFIS algorithm will also be used in this study to predict the permeability value of the reservoir. Data from well logging measurements will be used as the input, and core data from laboratory will be used as train data to validate prediction results of permeability values in the well depths domain. So, permeability value and pore type variations in the well depth domain will be obtained.