Enhancing formation resistivity factor estimation in carbonate reservoirs using electrical zone indicator and multi-resolution graph-based clustering methods.

The complex pore structure of carbonate rocks often results in scattered data in the relationship between formation resistivity factor (FRF) and porosity, posing significant challenges for accurate reservoir characterization. Although traditional methods have been useful, they exhibit limitations in reducing data variability particularly in capturing the critical interaction between the cementation factor and porosity. The Electrical Zone Indicator (EZI) represents a methodological advancement; however, greater precision is needed to achieve comprehensive rock typing resolution. In this study, Multi Resolution Graph-Based Clustering (MRGC) was integrated with the EZI rock typing method to improve FRF prediction accuracy in carbonate reservoirs. Well log data from three wells (A, B, and C) in a geologically complex carbonate reservoir in southwestern Iran were analyzed. To optimize data quality and consistency, rigorous preprocessing steps were applied, including depth shifting, data purification, and Principal Component Analysis (PCA). Using the MRGC method, five distinct electrofacies were identified and systematically incorporated into the refined EZI framework. Key petrophysical parameters tortuosity factor (a) and cementation exponent (m) were recalculated, yielding values in close agreement with established ranges for carbonate formations. The integration of MRGC with EZI led to substantial improvements in model performance, increasing the coefficient of determination (R²) for FRF estimation from 0.924 to 0.974. This enhanced workflow offers more accurate representation of petrophysical variability, improved precision in rock classification, and a robust framework for characterizing subsurface heterogeneities. The integration of advanced clustering techniques with electrical rock typing establishes a new benchmark for the classification of complex carbonate reservoirs, contributing to optimized hydrocarbon recovery strategies and more reliable reservoir management and fluid flow prediction.