Perpustakaan Digital ITB

flow in porous media. These are determined based on classification of similar logs among different groups of logging data. Data classification is accomplished by different statistical analysis such as principal component analysis, cluster analysis and differential analysis. The aim of this study is to predict 3D FZI (flow zone index) and Electrofacies (EFACT) volumes from a large volume of 3D seismic data. This study is divided into two parts. In the first part of the study, in order to make the EFACT model, nuclear magnetic resonance (NMR) log parameters were employed for developing an Electrofacies diagram based on pore size distribution and porosity variations. Then, a graph-based clustering method, known as multi resolution graph-based clustering (MRGC), was employed to classify and obtain the optimum number of Electrofacies. Seismic attribute analysis was then applied to model each relaxation group in order to build the initial 3D model which was used to reach the final model by applying Probabilistic Neural Network (PNN). In the second part of the study, the FZI 3D model was created by multi attributes technique. Then, this model was improved by three different artificial intelligence systems including PNN, multilayer feed-forward network (MLFN) and radial basis function network (RBFN). Finally, models of FZI and EFACT were compared. Results obtained from this study revealed that the two models are in good agreement and PNN method is successful in modeling FZI and EFACT from 3D seismic data for which no Stoneley data or NMR log data are available. Moreover, they may be used to detect hydrocarbon-bearing zones and locate the exact place for producing wells for the future development plans. In addition, the result provides a geologically realistic spatial FZI and reservoir facies distribution which helps to understand the subsurface reservoirs heterogeneities in the study area