S Gholipour; A Kadkhodaie; M Makkipour; A .R Abadi chalaksaraee
Abstract
Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial ...
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Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial neural network to calculate the organic carbon content. In the second stage, total organic carbon was calculated by using ΔLogR computational method. Finally in the last stage, well log data were classified into a set of electrofacies, which were performed using the most efficient clustering analysis method, i.e. MRGC method. Based on cluster validity tests, this method is the best to cluster petrophysical data in certain electrofacies. Cluster analysis was employed for classification of data from both neural network and ΔLogR methods. The results showed that intelligent systems are more appropriate than traditional techniques which are based on ΔLogR approaches, and also have higher accuracy. The proposed method has been presented with a case study from the Azadegan oilfield.
M. Faryabi; N. Kalantari; A. Negarestani
Abstract
The Jiroft plain is located at about 230 km from Kerman city in southeast of Iran. As groundwater is the main source for agriculture, industry and drinking in this area, thus its qualitative evaluation is very important. In this study for evaluation of groundwater chemical quality, a combination of statistical ...
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The Jiroft plain is located at about 230 km from Kerman city in southeast of Iran. As groundwater is the main source for agriculture, industry and drinking in this area, thus its qualitative evaluation is very important. In this study for evaluation of groundwater chemical quality, a combination of statistical method such cluster analysis and correlation coefficients and hydro geochemical methods such ionic ratios and composition diagrams were used. Groundwater samples were grouped with the use of Cluster Analysis method and similar samples were identified. On the basis of cluster analysis results, the groundwater samples fall into four groups, in other words the aquifer has been divided into four zones and each zone has peculiar chemical characteristics. In this paper ionic ratios of (Na+K-Cl)/ (Na+K-Cl+Ca), Na/ (Na+Cl), Mg/ (Ca+Mg), Ca/ (Ca+SO4), Cl/ (sum anions) and HCO3/ (sum anions) and composition diagrams for the characterizing groundwater influencing factors were used. Based on the obtained results, processes such as dissolution of gypsum and halite, Na-rich plagioclase weathering and ion exchange affect the groundwater quality of the study area.
