جستجوی مقالات مرتبط با کلیدواژه « مخزن سروک » در نشریات گروه « مهندسی شیمی، نفت و پلیمر »

Correlation of oils to each other and to their source rock is an important factor in defining a basin’s petroleum system. In this paper, 4 oil samples (#A13, #A09, #A04, #A03) from Sarvak reservoir and 2 source rocks (#SO1, #SO2) from Ahmadi member of Sarvak Formation in an oil field at southwest of Iran, were evaluated geochemically with Rock-Eval pyrolysis, column chromatography, gas chromatography (GC) and gas chromatography mass spectrometry (GC-MS) instruments. Moreover, thin section studies and Rock-Eval analysis along with biomarker data on source rock of Ahmadi member from Sarvak Formation indicate deep marine condition and kerogen type II. Determination of saturate, aromatic, resin and asphaltene fractions on study samples show paraffinic composition with nearly high thermal maturity for oils and naphtenic composition for extracted bitumen of probable source rock in studied oil fields. Geochemical studies indicate that the source rock of oil samples have carbonate-shale lithology in an anoxic marine environment. The biomarker ratios also show the medium to high thermal maturity of studied samples. Finally, star diagram of normal alkane and biomarker ratios, triangle diagram of C27-C28-C29 steran, variation of CPI, Pr/(Pr+Ph), C32 S/(S+R), C30 (βα/αβ+βα), C23 TT/C30 αβ Hopane and Tm/Ts versus calculated vitrinite reflex indicate that there are nearly biomarker similarities between #A03, #A04 and #A13 and source rock and can show genetic relationship. While #A09 oil sample with more space related to other samples might indicate more than one oil families in Sarvak reservoir of studied oil fields.

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.