A novel approach by integrating the core derived FZI and well logging data into artificial neural network model for improved permeability prediction in a heterogeneous gas reservoir

Predicting and extrapolating the permeability between wells to obtain the 3D distribution for the geological model, is a crucial and challenging task in reservoir simulation. Permeability is influenced by both digenetic characteristics and depositional factors like sorting and grain size. Hence, a reliable model should consider these characteristics for prediction of permeability. Grouping the rocks into different hydraulic flow units (HFU) or discrete rock types (DRT), improves the identity of the reservoir characteristics and provide a more accurate permeability prediction. Multi variable regression models and Artificial Neural Networks (ANN) were applied in this study to correlate core permeability and porosity with well logs to predict permeability logs. It was observed that the accuracy of the models diminished in heterogeneous reservoirs, where there is a wide permeability distribution.In this study, we are presenting a novel approach to predict permeability in heterogeneous oil and gas reservoirs. In this method the core permeability and porosity data are categorized using the concept of DRT and the probability density functions are used to investigate the relationships between the logs and DRT groups. The ANN model is applied to correlate the core derived flow zone indicator (FZI) with wire-line logging data with a single key well to predict K-logs. In this approach one single well, which contains all DRT groups is considered as a key well to develop and train the ANN model. It was observed that ANN model exhibits better prediction performance in heterogeneous reservoirs when it is developed and trained on single well data containing all DRT groups. This approach can capture heterogeneity in the reservoirs where it has been applied successfully to predict permeability in an actual heterogeneous carbonate gas reservoir.