Application of Cluster Analysis of Oil Geochemical Data to Determine the Connectivity of Sarvak Reservoir in One of the Fields of Southwest Iran

In the present paper, the cluster analysis of the molecular geochemical data of the oil has been used to determine the connectivity of Sarvak reservoir zones in one of the Iranian oil fields. Moreover, these methods include Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and K-means. Statistical analysis was performed on peak ratios of High-Resolution Gas Chromatography (HRGC) and biomarker data from Gas Chromatography - Mass Spectrometry (GC-MS). The results indicated the strong similarity between cross-plot of PC1 against PC2 from the ratio of the peaks of gas chromatography with that from the biomarker data. On the other hand, a cluster analysis by the HCA that separated the oil samples in exactly the same way as the PCA method was performed for biomarkers and gas chromatography peaks. After plotting several parameters of the biomarker maturity against the PC1 axis, it was found that 1) there is a direct relationship between them and 2) the oil groups are distinct from each other. These relationships indicate the effectiveness of the Principal Component Analysis (PCA) method for the study of Sarvak reservoir oil samples in the studied field. The results show that thermal maturity was essentially the main factor in the data clustering in this study. This issue was finally identified after performing the PCA statistical method on the data. Finally, drawing a diagram of biomarkers of maturity versus PC1 axis showed that the differences in maturity level of oils resulted in different oil groups. According to similarity the HRGC data clustering to the biomarker clustering results and other parameters obtained, presence of different oil families might indicate no/weak connectivity in the Saravk reservoir in the studied oil filed.