Flow Zone Indicator Estimation Based on Petrophysical Studies Using an Artificial Neural Network in a Southern Iran Reservoir

Porosity is a volumetric parameter whereas permeability is a measure of a rock's flow properties and depends on pore distribution and connectivity. Thus zonation of a reservoir using flow zone indicator (FZI) can be used to evaluate reservoir quality based on porosity-permeability relationships. The objective of this study was to develop an accurate reservoir FZI with the aid of artificial neural network (ANN) utilizing available geophysical well log data and dipole sonic imager (DSI) derived body wave velocities. The efficiency of utilizing shear wave and compressional wave velocities (V_p and V_s ) in improving estimation accuracy has been evaluated as well. It is the core data were used for ANN training that involves the calculations of Reservoir Quality Index, normalized porosity (? _z ), and FZI. Correlation between FZI calculated from core data and that obtained from well log data showed that ANN model were successful for estimation of FZI from conventional well log data. The compressional wave velocity was more effective than shear ones in delivering more accurate responses to estimate FZI. On the other hand, in association with other logs, utilizing compressional and shear wave velocities caused the responses to be closer to the reality and decrease the estimation error.