3D modeling of reservoir electrofacies using integration clustering and geostatistic method in central field of Persian Gulf

Abstract

Description and distribution of reservoir rock characterization in 3D space are challenging issues in study of heterogeneous reservoirs. In order to obviate these complications, a static model based on reservoir and geology characterization is created. Well log data has been using to extract reservoir properties. Analyzing a large volume of well log data in order to extract reservoir properties by manual approaches is difficult and time consuming. Therefore, reservoir properties can be determined by simultaneous process of several logs. The simultaneous process to classify well log data based on similar response is called electrofacies (EF). Electrofacies analysis is used to determine reservoir properties based upon lithology, porosity and permeability. This study consists of three steps. Initially, based on similarity in well log response and geological characteristics, electrofacies were created by unsupervised clustering approach in one well. These approaches include hierarchical cluster analysis (HCA), multi resolution graph based clustering (MRGC) and Self-Organizing Maps (SOM). Afterward, the clusters were generalized to all wells by supervised clustering approach. Finally, geostatistical simulation was applied to generate a 3D spatial model of reservoir electrofacies. According to the results, the hierarchical clustering has performed as a robust and more effective approach in clustering of data based on silhouette validity tests and geological information. Application of sequential indicator simulation (SIS) algorithms to estimate EF 3D model was successfully tested in the study area.