Integrated Clustering and Electrofacies Analysis for Reservoir Quality and Heterogeneity Assessment: A Case Study from a Southern Iranian Gas Field

The efficient characterization of heterogeneous carbonate reservoirs remains a significant challenge due to complex depositional environments and diagenetic alterations. While traditional methods like electrofacies analysis and clustering techniques offer inherent benefits, they often yield incomplete or conflicting results if used solely. This paper suggests an integrated study using petrophysical, geological, and statistical analyses to improve reservoir characterization. The proposed approach was applied to a carbonate reservoir case study of a gas field in South Iran. Well-log data and core samples were employed for detailed petrographic and petrophysical analyses. Electrofacies analysis using multi-resolution graph-based clustering (MRGC) identified five distinct electrofacies. Clustering techniques, including K-means and Gaussian mixture models (GMMs), were applied to petrophysical data to delineate similar zones. The Silhouette coefficient was used to evaluate the quality of the clusters. Results showed strong correlation between electrofacies 5 and clusters 4 (from K-means) and 5 (from GMMs), implying the best reservoir properties. This integrated approach suggested a more accurate assessment of reservoir quality attributes (e.g., porosity and water saturation) and highlighted the importance of dolomitized ooid grainstone in controlling hydrocarbon accumulation. This study provides a comprehensive framework for efficiently characterizing heterogeneous carbonate reservoirs by combining petrophysical, geological, and statistical methods. This integrated approach, validated through its successful application in similar reservoir studies, enables a more accurate assessment of reservoir quality attributes such as porosity and water saturation. By leveraging the complementary strengths of these methods, the approach ensures a comprehensive understanding of reservoir heterogeneity and its impact on hydrocarbon accumulation. Additionally, it is beneficial for improving reservoir modeling, enhancing hydrocarbon recovery, and reducing exploration risks.