Gas compressibility factor or z-factor plays an important role in many engineering applications related to oil and gas exploration and production, such as gas production, gas metering, pipeline design, estimation of gas initially in place (GIIP), and ultimate recovery (UR) of gas from a reservoir. There are many z-factor correlations which are either derived from Equation of State or empirically based on certain observation through regression analysis. However, the results of the z-factor obtained from different correlations have high level of variance for the same gas sample under the same pressure and temperature. It is quite challenging to determine the most accurate correlation which provides accurate estimate for a range of pressures, temperatures, and gas compositions. This paper presents a novel method to accurately estimate GIIP of an Australian tight gas field through identification of the most appropriate z-factor correlations, which can accurately determine the z-factor and other PVT properties for a wide range of gas compositions, temperatures, and pressures. The sensitivity study results demonstrated that a single correlation cannot work across the range of pressures and temperatures for a certain gas sample necessary to calculate z-factor during simulation process and/or other analysis, such as material balance and volumetric estimate.
Objective(s): To assess Baghdad University students’ knowledge and attitudes toward HIV/AIDS, and to find out
the relationship of Baghdad University students’ knowledge and attitudes with certain variables (gender,
socioeconomic status, field of study).
Methodology: A descriptive analytic study was used to assess the knowledge and attitudes of Baghdad University
Students’ toward HIV/AIDS. The study was conducted (November 1st 2012 to July 15th 2013). A non-probability
(purposive sample) of 400 students (males-138 and females-262) were selected from four colleges and they were
in the fourth class, a probability (stratified random) method was used to select four colleges at University of
Baghdad as a study settin
Iraqi bentonite is used as main material for preparing ceramic samples with the additions of alumina and magnesia. X-ray diffractions analyses were carried out for the raw material at room temperature. The sequence of mineral phase's transformations of the bentonite for temperatures 1000 ,1100 ,1200 and 1250 ºC reflects that it finally transformed in to mullite 39.18% and cristobalite 62.82%. Samples of different weight constituent were prepared. The effect of its constitutional change reveals through its heat treatments at 1000,1100,1200,1250and 1300ºC .The samples of additions less than 15% of alumina and magnesia could not stand up to 1300ºC while the samples of addition more than 15% are stable .That is shown by analy
... Show MoreNonsteroidal anti-inflammatory drugs (NSAIDs) are drugs that help reduce inflammation, which often helps to relieve pain. In this research new ibuprofen oxothiazolidnone derivatives were synthesized from the reaction of Schiff base derivatives of Ibuprofen with mercapto acetic acid VI a-c, to improve the potency and to decrease the drug's potential side effects, a new series of 4-thiazolidinone derivatives of ibuprofen was synthesized VI a-c . The characterizations of the compounds were identified by using FTIR, 1HNMR technique and by measuring the physical properties.
Cocoon of larva
Two different composite materials were prepared by stir casting method of AA 6061 alloy as a matrix reinforced with two addition different ceramic materials Al2O3 and B4C of grain size 20 µm by 2.5, 5, 7.5 and10% in weight. The composite material with aluminum alloy as a matrix possesses a unique mechanical properties such as: high specific strength and hardness, low density, and high resistance to corrosion and friction wear. This composite is widely used in automotive parts space and marine applications.
Pin-on-disc technique was used to calculate the wear rate for each addition of Al2O3 and B4C particles. Rockwell hardness test and
... Show MoreThe meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
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