Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods.

Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

Hydraulic flow unit and rock types of the Asmari Formation, an application of flow zone index and fuzzy C-means clustering methods.

Rock types are the reservoir's most essential properties for special facies modeling in a defined range of porosity and permeability. This study used clustering techniques to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran. Four hydraulic flow units (HFUs) were determined for studied data utilizing histogram analysis, normal probability analysis, and the sum of squared errors (SSE) statistical methods. Then, two flow zone index (FZI) and fuzzy c-means (FCM) clustering methods were used to determine the rock types in the given well according to the results obtained from the HFU continuity index acts in-depth. The FCM method, with a continuity number of 3.12, compared to the FZI, with a continuity number of 2.77, shows more continuity in depth. The relationship between permeability and porosity improved considerably by utilizing HFU techniques. This improvement is achieved using the FZI method study. Generally, all samples increased from 0.55 to 0.81 in the first HFU and finally to 0.94 in the fourth HFU. Similar flow properties in an HFU characterized the samples. In comparison, the correlation coefficients obtained in the FCM method are less than those in the general case of all HFUs. This study aims to determine the flowing fluid in the porous medium of the Asmari reservoir employing the c-mean fuzzy logic. Also, by determining the facies of the rock units, especially the siliceous-clastic facies and log data in the Asmari Formation, the third and fourth flow units have the highest reservoir quality and permeability. Results can be compared to determining HFU in nearby wellbores without cores.

Identifying the source of petroleum pollution in sediment cores of southwest of the Caspian Sea using chemical fingerprinting of aliphatic and alicyclic hydrocarbons.

In this study, the concentration and sources of aliphatic and petroleum markers were investigated in 105 samples of Anzali, Rezvanshahr and Astara cores from the southwest of Caspian Sea. Petroleum importation was diagnosed as a main source in most depths of cores by the results of unresolved complex mixture, carbon preference index and hopanes and steranes. From the chemical diagnostic parameters, petroleum inputs in sediment of cores were determined to be different during years and the sources of hydrocarbons in some sections differed than Anzali and Turkmenistan and Azerbaijan oils. Diagenic ratios in most sediments of upper and middle sections in Astara core were determined to be highly similar to those of Azerbaijan oil, while the presence of Turkmenistan and Anzali oils were detected in a few sections of Anzali and Rezvanshahr cores and only five layers of downer section in Anzali core, respectively.

Identification of sources of tar balls deposited along the Southwest Caspian Coast, Iran using fingerprinting techniques.

In 2012, a significant number of tar balls occurred along the Southwest coasts of the Caspian Sea (Iran). Several oil fields of Turkmenistan, Azerbaijan and Iran might be sources of oil spills and lead to the formation of these tar balls. For source identification, 6 tar ball samples were collected from the Southwest beaches of the Caspian Sea and subjected to fingerprint analysis based on the distribution of the source-specific biomarkers of pentacyclic tri-terpanes and steranes. Comparing the diagenic ratios revealed that the tar balls were chemically similar and originated from the same source. Results of double ratio plots (e.g., C29/C30 versus ∑C31-C35/C30 and C28 αßß/(C27 αßß+C29 αßß) versus C29 αßß/(C27 αßß+C28 αßß)) in the tar balls and oils from Iran, Turkmenistan and Azerbaijan indicated that the tar balls might be the result of spills from Turkmenistan oil. Moreover, principle component analysis (PCA) using biomarker ratios on the tar balls and 20 crude oil samples from different wells of Azerbaijan, Iran and Turkmenistan oils showed that the tar balls collected at the Southwest beaches are highly similar to the Turkmenistan oil but one of the Azerbaijan oils (from Bahar field oils) was found to be also slightly close to the tar balls. The weathering characterizations based on the presence of UCM (unresolved complex mixture) and low/high molecular weight ratios (L/H) of alkanes and PAHs indicated the tar ball samples have been significantly influenced by natural weathering processes such as evaporation, photo-degradation and biodegradation. This is the first study of its kind in Iran to use fingerprinting for source identification of tar balls.

Distribution and origins of n-alkanes, hopanes, and steranes in rivers and marine sediments from Southwest Caspian coast, Iran: implications for identifying petroleum hydrocarbon inputs.

The occurrence of n-alkanes and biomarkers (hopane and sterane) in surface sediments from Southwestern coasts of Caspian Sea and 28 rivers arriving to this lake, determined with a gas chromatography-mass spectrometry method, was used to assess the impacts of anthropogenic activities in the studied area. The concentrations of total n-alkanes (Σ21 n-alkane) in costal and riverine sediments varied from 249.2 to 3899.5 and 56 to 1622.4 µg g(-1), respectively. An evaluation of the source diagnostic indices indicated that petroleum related sources (petrogenic) were mainly contributed to n-alkanes in costal and most riverine sediments. Only the hydrocarbons in sediment of 3 rivers were found to be mainly of biogenic origin. Principal component analysis using hopane diagnostic ratios in costal and riverine sediments, and Anzali, Turkmenistan, and Azerbaijan oils were used to identify the sources of hydrocarbons in sediments. It was indicated that the anthropogenic contributions in most of the costal sediment samples are dominated with inputs of oil spills from Turkmenistan and Azerbaijan countries.