Prediction of pore facies using GMDH-type neural networks: a case study from the South Pars gas field, Persian Gulf basin

Pore facies analysis plays an important role in the classification of reservoir rocks and reservoir simulation studies. The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalan formations in the South Pars gas field. In the first step, pore facieswere determined based on Mercury Injection Capillary Pressure (MICP) data in corporation with the Hierarchical Clustering Analysis (HCA) method. Each pore facies represents a specific type of pore geometry indicating the interaction between the primary rock fabric and its diagenetic overprints. In the next step, polynomial meta-models were established based on the evolved Group Method of Data Handling (GMDH) neural networks for the purpose of pore facies identification from well log responses. In this way, the input data table used for training GMDH-type neural network consists of CALI, GR , CGR , SGR, DT, NPHI, RHOB, PEF, PHIE and VDL logs. The MICP-HCA derived pore facies were considered as the desired outputs. Moreover, multi-objective genetic algorithms (GAs) are used to evolutionary design of GMDH-type neural networks. Training error and prediction error of neural network have been considered as conflicting objectives for Pareto multi-objective optimization. The results of this study indicate the successful implementation of GMDH neural networks for classification of pore faciesin the heterogeneous gas bearing carbonate rocks of South Pars gas field.