The current study focuses on the assessment of pollution indicators and health risks of heavy elements in the surface soil of Samarra City. Twelve soil sample collected from different sites in Samarra City, analysis of soil sample to find the heavy metals concentrations  which As, Br, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sn, V, Zn, Zr, U, B, Cd, Hg, Th, Ce, La, Th, B, Ba .The results are compared with limit of world standard (12). The higher values which refer to pollution in heavy metal are  Cr, Cu, Ni, Zn, Zr, Cd due to industry activity and Hg higher concentration because of Pharmaceutical Industries and Medical Waste . The high concentration in V, Br, Mo, Se, As  because of agriculture activity. The enrichment factor calculated

       Modern cities suffer from heavy metal pollution due to urban expansion and population increase. Heavy metals have a great impact on human health. The objective is to determine the contamination level of heavy metals Cr, Mn, Ni, Cu, Zn, As, Zr and Pb at industrial and residential in Hilla city. The mean concentration of Cr, Mn, Ni, Cu, Zn, As, Zr and Pb enrichment factors of the investigated industrial soils are 3.43, 0.74, 6.45, 3.95, 5.60, 3.44, 1.17 and 11.44, respectively. The means of Cr, Mn, Ni, Cu, Zn, As, Zr and Pb in residential soils are 3.30, 1.09, 11.40, 0.94, 2.08, 5.39, 0.9 and 3.6, respectively. The I-geo mean values of heavy elements in the industrial area may be ordered in the following: Mn> Pb> Ni>

       The present study was performed to spotlight the potential role of soil bacteria in the Al-Rumaila oil field as a bioindicator of heavy metals pollution. For this purpose, nine soil samples were collected from different sites, with 20cm depth, to assess the pollution status depending on the total and available concentrations of heavy metals.  The result indicates pollution of the studied soils with the following metals: Cd, Cu, Fe, Zn, and Pb. The mean of total concentration for all studied metals was higher than the allowed maximum limit based on the international limit:(3.394, 3.994, 39.993, 8844.979,150.372, and 103.347 µg/g), respectively. While measuring the total Metal concentration is important in determining the de

This study investigated the applicability of iron oxide (Fe₂O₃) nanoparticles for the removal of cadmium metal from sewage water by using batch scale experiments. The iron oxide nanoparticles of 27.7nm were synthesized using a biological method and characterized by Atomic Force Microscope (AFM).  The Box-Wilson design was used to conduct experiments with three parameters such as pH (2-6), time of adsorption (6-120min) and adsorbent dosage (5-25mg/L). The best conditions occurred at pH: 5.5; contact time: 95.8 min; and iron oxide nanoparticle dosage: 20.77 mg/L for maximum cadmium removal of (96.9%).

     The study area lies in Wasit governorate south west Kut city, where Al Ahdeb oil field is located to the south of Al Ahrar district. The present study deals with assessment of heavy metals pollution in water by collecting eleven water samples (five samples from drainage and six samples from surface water) in 5^th of December 2016. The water samples analysis of heavy elements in the study area shown that water is polluted with high concentration of (Pb, Cd) elements, while the concentration of (Cu, Fe, Zn) in water samples are within the permissible limits of both World Health Organization, and Iraqi standards and there are no hazard effects from these elements. The study detected some diseases that inju

Samples of gasoline engine oil (SAE 5W20) that had been exposed to various oxidation times were inspected with a UV-Visible (UV-Vis) spectrophotometer to select the best wavelengths and wavelength ranges for distinguishing oxidation times. Engine oil samples were subjected to different thermal oxidation periods of 0, 24, 48, 72, 96, 120, and 144 hours, resulting in a range of total base number (TBN) levels. Each wavelength (190.5 – 849.5 nm) and selected wavelength ranges were evaluated to determine the wavelength or wavelength ranges that could best distinguish among all oxidation times. The best wavelengths and wavelength ranges were analyzed with linear regression to determine the best wavelength or range to predict oxidation t

This paper discusses the method for determining the permeability values of Tertiary Reservoir in Ajeel field (Jeribe, dhiban, Euphrates) units and this study is very important to determine the permeability values that it is needed to detect the economic value of oil in Tertiary Formation. This study based on core data from nine wells and log data from twelve wells. The wells are AJ-1, AJ-4, AJ-6, AJ-7, AJ-10, AJ-12, AJ-13, AJ-14, AJ-15, AJ-22, AJ-25, and AJ-54, but we have chosen three wells (AJ4, AJ6, and AJ10) to study in this paper. Three methods are used for this work and this study indicates that one of the best way of obtaining permeability is the Neural network method because the values of permeability obtained be

Smart water flooding (low salinity water flooding) was mainly invested in a sandstone reservoir. The main reasons for using low salinity water flooding are; to improve oil recovery and to give a support for the reservoir pressure.

In this study, two core plugs of sandstone were used with different permeability from south of Iraq to explain the effect of water injection with different ions concentration on the oil recovery. Water types that have been used are formation water, seawater, modified low salinity water, and deionized water.

The effects of water salinity, the flow rate of water injected, and the permeability of core plugs have been studied in order to summarize the best conditions of low salinity

   History matching is a significant stage in reservoir modeling for evaluating past reservoir performance and predicting future behavior. This paper is primarily focused on the calibration of the dynamic reservoir model for the Meshrif formation, which is the main reservoir in the Garraf oilfield. A full-field reservoir model with 110 producing wells is constructed using a comprehensive dataset that includes geological, pressure-volume-temperature (PVT), and rock property information. The resulting 3D geologic model provides detailed information on water saturation, permeability, porosity, and net thickness to gross thickness for each grid cell, and forms the basis for constructing the dynamic reservoir model. The dynamic reservoir mo