Binary mixtures of three, heavy oil-stocks was subjected to density measurements at temperatures of 30, 35 and 40 °C. and precise data was acquired on the volumetric behavior of these systems. The results are reported in terms of equations for excess specific volumes of mixtures. The heavy oil-stocks used were of good varity, namely 40 stock, 60 stock, and 150 stock. The lightest one is 40 stock with °API gravity 33.69 while 60 stock is a middle type and 150 stock is a heavy one, with °API gravity 27.74 and 23.79 respectively. Temperatures in the range of 30-40 °C have a minor effect on excess volume of heavy oil-stock binary mixture thus, insignificant expansion or shrinkage is observed by increasing the temperature this effect beco

This work deals with preparation of zeolite 5A from Dewekhala kaolin clay in Al-Anbar region for drying and desulphurization of liquefied petroleum gas. The preparation of zeolite 5A includes treating kaolin clay with dilute hydrochloric acid 1N, treating metakaolin with NaOH solution to prepare 4A zeolite, ion exchange, and formation. For preparation of zeolite 4A, metakaolin treated at different temperatures (40, 60, 80, 90, and 100 °C) with different concentrations of sodium hydroxide solution (1, 2, 3, and 4 N) for 2 hours. The zeolite samples give the best relative crystallinity of zeolite prepared at 80 °C with NaOH concentration 3N (199%), and at 90 and 100°C with NaOH concentration solution 2N (184% and 189%, respectively). Ze

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Background: diagnostic radiology field workers are at elevated risk level for systemic and oral diseases like periodontal diseases. This study was aimed to estimate the periodontal condition and salivary flow rate among diagnostic radiology workers. Material and method: The sample for this study consisted of a study group radiographers (forty subjects) working for 5 years at least and control group consisted of nurses and laboratory workers away from radiation (forty subjects) in Baghdad hospitals. All the 80 subjects aged 30-40 year-old and looking healthy without systemic diseases. Plaque, gingival, periodontal pocket depth and clinical attachment loss indices were used for recording the periodontal conditions. Under standardized condi

Existing leachate models over–or underestimates leachate generation by up to three orders of magnitude. Practical experiments show that channeled flow in waste leads to rapid discharge of large leachate volumes and heterogeneous moisture distribution. In order to more accurately predict leachate generation, leachate models must be improved. To predict moisture movement through waste, the two–domain PREFLO, are tested. Experimental waste and leachate flow values are compared with model predictions. When calibrated with experimental parameters, the PREFLO provides estimates of breakthrough time. In the short term, field capacity has to be reduced to 0.12 and effective storage and hydraulic conductivity of the waste must be increased to

In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H