Biostratigraphy,Microfacies, Sedimentary Environments and Sequence Stratigraphy of the Late Devonian–Carboniferous Deposits at the Anarak Section,Central Iran

The Late Devonian–early Carboniferous deposits of the Anarak section in northeastern Isfahan, Central Iran, evaluated based on conodont biostratigraphy, sedimentary environment and sequence stratigraphy. According to the field observations, five lithological units were identified. Investigating the conodont fauna of the Late Devonian–Carboniferous(Mississippian–Pennsylvanian) deposits of Bahram, Shishtu, and Qaleh(Sardar 1) formations in Anarak section led to the identification of 67 species of ...     

Gorgan Bay:a microcosm for study on macrobenthos species-environment relationships in the southeastern Caspian Sea

The relationship between spatial patterns of macrobenthos community characteristics and environmental conditions(salinity, temperature, dissolved oxygen, organic matter content, sand, silt and clay) was investigated throughout the Gorgan Bay in June 2010. Principal components analysis(PCA) based on environmental data separated eastern and western stations. The maximum(4500 ind./m²) and minimum(411 ind./m²) densities were observed at Stas 1 and 6, respectively. Polychaeta was the major group and Streblospio gynobranchiata was dominant species in the bay. According to Distance Based Linear Models results, macrofaunal total density was correlated with silt percentage and salinity and these two factors explaining 64% of the variability while macrofaunal community structure just correlated with salinity(22% total variation). In general, western part of the bay showed the highest number of species and biodiversity while, the highest density was found at Sta. 1 and in the middle part of the bay. Furthermore, relationship between diversity indices and macrobenthic species with measured factors is also discussed. Our results confirm the effect of salinity as an important factor on distribution of macrobenthic fauna in south Caspian brackish waters.     

Biostratigraphy and Sequence Stratigraphy of the Gurpi Formation at Deh Dasht Area,Zagros Basin,SW Iran

Abstract: A rich assemblage of planktonic foraminifera has been studied from an outcrop of the Gurpi Formation, the hydrocarbon source rock in the southwest Iran, Deh Dasht area (Kuh-e Siah anticline). Based on the distribution of the planktonic foraminifera, eight biozones have been recognized that included: Dicarinella concavata Interval Zone (Earliest Santonian), Dicarinella asymetrica Total Range Zone (Santonian to Earliest Campanian), Globotruncanita elevata Partial Range Zone (Early Campanian), Globotruncana ventricosa Interval Zone (Middle to Late Campanian), Radotruncana calcarata Total Range Zone (Late Campanian), Globotruncanella havanensis Partial Range Zone (Late Campanian), Globotruncana aegyptiaca Interval Zone (Late to latest Campanian), Gansserina gansseri Interval Zone (Latest Campanian to Early Maastrichtian). These biozones indicates that the Gurpi Formation deposited during the Early Santonian- Early Maastrichtian. These biozones are compared to the most standard biozones defined in Tethysian domain. Based on distribution of morphotype groups of planktonic foraminifera, planktonic to benthic ratio (P/B) and content of carbonate, nine third-order sequences are recognized.     

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model-based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model-based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi-layer feed-forward network (MLFN) and radial basic function network (RBFN).