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Multiphase Flow Behavior Prediction and Optimal Correlation Selection for Vertical Lift Performance in Faihaa Oil Field, Iraq
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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 (HB) correlation provides the most accurate correlation for calculating pressure in FH-1 and FH-3 while the Beggs and Brill original (BBO) correlation proves to be the optimal fit for wells FH-2 and Gomez mechanistic model for FH-4. These correlations show the lowest root mean square (RMS) values of 11.5, 7.56, 8.889, and 6.622 for the four wells, respectively, accompanied by the lowest error ratios of 0.00692%, 0.00033%, 0.00787%, and 0.0011%, respectively. Conversely, Beggs and Brill original (BBO) correlation yields less accurate results in predicting pressure drop for FH-1 compared with other correlations. Similarly, correlations, such as Orkiszewski for FH-2, Duns and Ros for FH-3, and ANSARI for FH-4, also display less accuracy level. Notably, the study also identifies that single-phase flow dominates within the tubing string until a depth of 6000 feet in most wells, beyond which slug flow emerges, introducing significant production challenges. As a result, the study recommends carefully selecting optimal operational conditions encompassing variables such as wellhead pressure, tubing dimensions, and other pertinent parameters. This approach is crucial to prevent the onset of slug flow regime and thus mitigate associated production challenges.

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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Wed Dec 30 2009
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of the Point Efficiency of Sieve Tray Using Artificial Neural Network
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An application of neural network technique was introduced in modeling the point efficiency of sieve tray, based on a
data bank of around 33l data points collected from the open literature.Two models proposed,using back-propagation
algorithm, the first model network consists: volumetric liquid flow rate (QL), F foctor for gas (FS), liquid density (pL),
gas density (pg), liquid viscosity (pL), gas viscosity (pg), hole diameter (dH), weir height (hw), pressure (P) and surface
tension between liquid phase and gas phase (o). In the second network, there are six parameters as dimensionless
group: Flowfactor (F), Reynolds number for liquid (ReL), Reynolds number for gas through hole (Reg), ratio of weir
height to hole diqmeter

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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Publication Date
Tue Jun 14 2022
Journal Name
Al-academy
The effectiveness of the deconstructive pattern in theatrical performance
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Theatrical art, from (Plato) to (Heidegger), passing through (Husserl) and (Husserl) has propagated the parallel relations between the overlapping formal patterns in the world of hypotheses crowded with diaspora and scattering, leading to the manifestations of implicit meaning in the intellectual and aesthetic discourse, through the deconstructive pattern that restructures The aesthetic image according to the aesthetic data to be employed, so the effectiveness of the deconstructive system had an important role in authorizing the Ghanaian logic and continuity on which the Western meta meaning was based, and the artistic scene was subjected to it in line with literature and art to be able to pay attention to the achievement and clarify it

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Publication Date
Thu Oct 23 2025
Journal Name
Baghdad Science Journal
Continuous Flow Injection-Turbidimetric Analysis for Determination of Cobalt (II) Ion in Alloys Using a Microcell with Irradiation Sources at 0–180° Using a Single Solar Cell as a Detector
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A cost-effective and efficient detector was created to conduct thorough turbidimetric measurements by reaction of Co (II) ion with calcium ferro cyanide to form bright green particulate, using the method of continuous flow injection analysis, the use of NAG-5SX1-1D-SSP Analyzer in determining cobalt (II) ion in a test for the validity of the new design. The NAG-5SX1-1D-SSP Analyzer is composed of five irradiation sources of white snow leds having the diameter of 10 mm with one solar cell of 55 mm length, 13.5 mm width. Using a selector switch to select the optimum voltage to be used which was 2.7 VDC. Under conditions of optimization, cobalt (II) ion was determined at 0.005–20 mmol. L–1(n = 23) while linearity dynamic range 0.005–7 mm

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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of International Society Of Preventive And Community Dentistry
The impact of dental environment stress on dentition status, salivary nitric oxide and flow rate
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Publication Date
Sun Mar 01 2020
Journal Name
Baghdad Science Journal
The Influence of Magnetohydrodynamic Flow and Slip Condition on Generalized Burgers’ Fluid with Fractional Derivative
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This paper investigates the effect of magnetohydrodynamic (MHD) of an incompressible generalized burgers’ fluid including a gradient constant pressure and an exponentially accelerate plate where no slip hypothesis between the burgers’ fluid and an exponential plate is no longer valid. The constitutive relationship can establish of the fluid model process by fractional calculus, by using Laplace and Finite Fourier sine transforms. We obtain a solution for shear stress and velocity distribution. Furthermore, 3D figures are drawn to exhibit the effect of magneto hydrodynamic and different parameters for the velocity distribution.

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Publication Date
Sun Mar 15 2020
Journal Name
Journal Of Baghdad College Of Dentistry
The impact of dental environment stress on caries experience, salivary flow rate and uric acid
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Background: Several pathologies of the oral cavity have been associated with stress. Dental students need to gain assorted proficiencies as theoretical knowledge, clinical proficiencies, and interpersonal dexterity which is accompanied with high level of stress. Uric acid is the major antioxidant in saliva. The aim of this study is to assess the dental caries experience among dental students with different levels of dental environment stress in relation to physicochemical characteristics of whole unstimulated saliva.

Materials and Methods: the total sample is composed of 300 dental students (73 males, 227 female) aged 22-23 years old, from collage of dentistry / university of Baghdad, from the 4t

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Publication Date
Tue Oct 13 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Study of salivary IgA concentrations, salivary flow rate in patients with β –thalassemia major in Missan Governorate
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Background: Beta-thalassemia major is the most common monogenic known disorder in the Middle East, characterized by anomalies in the synthesis of the beta chains of hemoglobin resulting in variable phenotypes ranging from severe anemia to clinically asymptomatic Individuals. This study aimed to evaluate salivary flow rate and salivary IgA in β-thalassemia major patients. Since many oral and systemic conditions manifest themselves as changes in the flow and composition of saliva the dental practitioner is advised to remain up-to-date with this issue. Materials and methods: The study samples consist of (60) subjects, patients group composed of (30) patients with β –thalassemia major, age rang (5-23) years and (30) healthy locking

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Publication Date
Fri Apr 01 2022
Journal Name
Chemical Engineering Science
Numerical modelling of flow through nodes in foams within the “dry” limit in the presence of solid particles
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