The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishrif Formation; MA, MB11, MB12, MB21, MC1, and MC2. The precise facies modelling is constructed by using Petrel software while applying different appropriate scale-up methods. Consequently, the petrophysical properties such as porosity, water saturation and permeability are distributed within each unit depending on facies modelling. The Net to a gross parameter which has a significant impact on determining original oil in place (OIIP) also calculated and distributed using facies modelling. The facies modelling is performed to obtain an accurate estimation of OIIP. Finally, the results of the facies investigation have a significant effect on petrophysical properties and therefore affect the estimation of OIIP by 2\% for the whole Mishrif Formation.
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Often phenomena suffer from disturbances in their data as well as the difficulty of formulation, especially with a lack of clarity in the response, or the large number of essential differences plaguing the experimental units that have been taking this data from them. Thus emerged the need to include an estimation method implicit rating of these experimental units using the method of discrimination or create blocks for each item of these experimental units in the hope of controlling their responses and make it more homogeneous. Because of the development in the field of computers and taking the principle of the integration of sciences it has been found that modern algorithms used in the field of Computer Science genetic algorithm or ant colo
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This paper introduces a Laplace-based modeling approach for the study of transient converter-grid interactions. The proposed approach is based on the development of two-port admittance models of converters and other components, combined with the use of numerical Laplace transforms. The application of a frequency domain method is aimed at the accurate and straightforward computation of transient system responses while preserving the wideband frequency characteristics of power components, such as those due to the use of high frequency semiconductive switches, electromagnetic interaction between inductive and capacitive components, as well as wave propagation and frequency dependence in transmission systems.
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In the last years, a new technology called Cloud computing has been developed. Empirical and previous studies, commonly examined in business field and other domains. In this study, the significant factors that affecting the adoption of cloud computing have been examined using a frequency analysis that have been explored by the previous studies. The results showed that the most effected factors were relative advantage which followed by security and privacy, complexity, innovativeness, and external support. In this study the model of technology organization-environment was used to examine the significant factors that affecting the adoption of cloud computing.
In this work, the nano particles of Na-A zeolite were synthesized by sol –gel method. The samples were characterized by X-ray diffraction (XRD), X-ray luorescence (XRF), Surface area and pore volume, Atomic Force Microscope (AFM) and Fourier Transform Infrared Spectroscopy (FTIR). Results show that the nano A zeolite is with average crystal size is 74.77 nm., Si/Al ratio 1.03, BET surface area was 581.211m2/g and the pore volume for NaA was found equal to 0.355cm3/g.
The specific activity of 29 soil samples collected from Fuel
Fabrication Facility FFF at AL-Tuwaitha site, 20 km south of
Baghdad were determined using HPGe detector in a low background
configuration, it's relative efficiency of 40%, and resolution of 2keV
for the 1332 keV gamma ray emission of 60Co. The range of activity
concentrations of 226Ra, 232Th and 40K were between (12.56-31.96),
(10.2-18.4) and (47.47-402.1) Bq/kg respectively. In order to assess
any radiological hazard to human health, the absorbed gamma dose
rate D in air at 1m above the ground surface was calculated in the
range (18.87 to 36.46) nGy/h; the outdoor annual effective dose
equivalent AEDE was evaluated to vary from 0.0039 to 0.0076
Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability
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