Exploration activities of the oil and gas industry generate loads of formation water called produced water (PW) up to thousands of tons each day. Depending on the geographic area, formation depth, oil production techniques, and age of oil supply wells, PW from different oil fields contain different chemical compositions. Currently, PW is also known as industrial waste water containing heavy metals that are toxic to humans and the environment, requiring special processing so that they can be disposed of in the environment. To determine the heavy metals content in PW from the Al-Ahdab oil field (AOF), the Ministry of Science and Technology/Agricultural Research Department determined some parameters including the concentrations of Cd, Co, Cr, Pd, and Ni using instrument inductively couple plasma (ICP-OES). Results of this study showed high concentrations of Cd (0.51-2.05, Cr (0.06-1.81), Co (0.11-0.72), Ni (0.12-0.22) and Pb (5.52-20.6) in the AOF compared to concentrations in water bodies about 16 km outside the field; Cd (0.01-0.32), Cr (0.01-0.11), Co (0.03-0.18), Ni (0.02-0.11) and Pb (0.04-1.73). These findings indicate there are increased levels of pollutants in the PW within the AOF of the Main Outfall Drain (MOD). The PW could not be as a source of drinking water and other daily activities, including fisheries and crop planting, unless advanced treatment, to remove the heavy metal content.
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria
The comparison of double informative priors which are assumed for the reliability function of Pareto type I distribution. To estimate the reliability function of Pareto type I distribution by using Bayes estimation, will be used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of Pareto type I distribution . Assuming distribution of three double prior’s chi- gamma squared distribution, gamma - erlang distribution, and erlang- exponential distribution as double priors. The results of the derivaties of these estimators under the squared error loss function with two different double priors. Using the simulation technique, to compare the performance for
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