List of Articles lithology

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  1 - The effect of precipitation and lithology on hydrochemical characteristics of the Karstic Springs in North Khorasan Province
  Fatemeh Bagheri Gholamhossein karami rahim bagheri Javad Meshkini
  Karstic aquifers are vital water resources which are used for irrigation and drinking purposes in arid and semi-arid regions. Understanding of the hydrogeological behavior of these springs and the qualitative tracing of these water resources are the first step in their More

  Karstic aquifers are vital water resources which are used for irrigation and drinking purposes in arid and semi-arid regions. Understanding of the hydrogeological behavior of these springs and the qualitative tracing of these water resources are the first step in their better management. There are wide outcrops of Tirgan karstic formation in the study area, in the north of Khorasan province. In this area, there are a few karstic springs the discharge rate ranges from 50 to 500 lit/s. The recharge area characteristics of these springs vary significantly with their elevation, catchment size, thickness of epikarst and degree of karstification. In this study, temporal and spatial hydrogeochemcal variations of the five karstic springs including Arnaveh, Rezghaneh, Estarkhi, Ghordanlo and Sarani and 3 rain stations are investigated during one year period. The effects of both precipitation and lithology on the chemistry of these karstic springs are also considered. The dominant rain water types are Ca-SO4-Cl and Ca-HCO3 which change into Ca-Mg- HCO3 type during ground water flow in karstic system. This karstic aquifer is recharged during winter snowfall. The EC values of the rainfall vary from 70 µmohs/cm in Namanloo station to 100 and 150 µmohs/cm in Estarkhi and Ghale Barbar stations, respectively. The summer precipitations have more EC value than winter precipitations. This is due to long trajectory of air masses through arid regions with dust particles. The time series variations of discharge value are negligible in some karstic springs except for Sarani and Estarkhi springs. Hydrochemical composition of Sarani, Ghordanlo and Estarkhi springs are mostly affected by precipitation; while, Arnaveh and Rezghaneh springs with the same precipitation composition in this area have higher EC values. This is due to soil cover in catchment area, dissolution of clay minerals and diffusion. Manuscript profile
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  2 - Petrophysical evaluation and determination of reservoir rock types in the Ghar member,the Abouzar oilfield, Persian Gulf.
  مهرناز نصیری محمدرضا رجلی نوده
  This study is aimed at petrophysical evaluation of the Ghar reservoir using Multimin method by Geolog software in five wells from the Abouzar oilfield. For this purpose, well log data comprising of neutron, density, sonic, gamma, resistivity and photoelectric absorption More

  This study is aimed at petrophysical evaluation of the Ghar reservoir using Multimin method by Geolog software in five wells from the Abouzar oilfield. For this purpose, well log data comprising of neutron, density, sonic, gamma, resistivity and photoelectric absorption were utilized and their analysis lead to determination of quantitative petrophysical properties such as porosity, volume of shale, water, oil saturation and qualitative parameters including lithology and clay mineral types. The analyses revealed that three zones could be identified in the Ghar reservoir. Meanwhile, there are three shaly interlayers within the Ghar foemation. By application of the cutoff values on oil in place (OIP), petrophysical properties were determined zone by zone and based on Net to Gross ratio (N/G) high reservoir quality zone was identified. Finally by using clustering algorithm, reservoir rock types were identified based upon six properties including density, neutron, gamma ray, volume of shale, water saturation and effective porosity. The facies were introduced on the basis of their priority in reservoir quality so that there is an agreement between petrophysical evaluation results and electrofacies. General lithology of the reservoir in composed of upper loose sands and consolidated sand in the lower part. The lower sands are consolidated by the calcite cement. Overall, the volume of clay minerals in the lower part is less than that of upper part. However, productive zones were separated by a thin shaly layer. The clay minerals type in the shaly layer differs from those present in the reservoir rocks. Total and effective porosity are almost identical which is due to low volume of shale. Manuscript profile
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  3 - Designing an Ensemble model for estimating the permeability of a hydrocarbon reservoir by petrophysical lithology Labeling
  abbas salahshoor Ahmad Gaeini Alireza shahin mossayeb kamari
  Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measu More

  Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measurements. Recently, machine learning algorithms have been used to predict permeability due to better predictability. In this study, a new ensemble machine learning model for permeability prediction in oil and gas reservoirs is introduced. In this method, the input data are labeled using the lithology information of the logs and divided into a number of categories and each category was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we were able to predict the accuracy of such a square mean error by designing a group model using ETR, DTR, GBR algorithms and petrophysical data. Improve dramatically and predict permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies. Manuscript profile