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Wellbore Instability Analysis to Determine the Safe Mud Weight Window for Deep Well, Halfaya Oilfield
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Wellbore instability is one of the most common issues encountered during drilling operations. This problem becomes enormous when drilling deep wells that are passing through many different formations. The purpose of this study is to evaluate wellbore failure criteria by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict a safe mud-weight window for deep wells. An integrated log measurement has been used to compute MEM components for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results. The prediction of mud weight along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations ranges between 12.5 to 15 ppg. The predicted safe mud weight value seems to be narrow with a well deviation higher than 350. Therefore, for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations, the wellbore appears unstable compared to other formations. The results of stability analyses indicate that the breakout mud weight wasn’t affected by wellbore azimuth because of low-stress contrast. Furthermore, shear failure can be prevented by drilling the well with an inclination of less than 350. As well as, to prevent breakdown the well should be drilled with an inclination between 25o to 65o in the direction of minimum horizontal stress. These outcomes could be used to prevent wellbore instability and determine a safe mud-weight window when planning to drill nearby wells in the future.

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Publication Date
Wed Oct 26 2022
Journal Name
Petroleum Science And Technology
Building 3D geological model using non-uniform gridding for Mishrif reservoir in Garraf oilfield
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Publication Date
Sat Sep 15 2018
Journal Name
Journal Of Baghdad College Of Dentistry
Prosthetic Status in Relation to Weight Status and Occupation among Parkinson's Disease Patients in Baghdad-Iraq
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Background: The symptoms of Parkinson's disease (PD) can lead to problems in movement and coordination that lead to difficulty in maintaining well oral cleaning which can then negatively affect dental status of those Patients. The aim of present study: To evaluate prosthetic status in relation to weight status and occupation by age and gender among Parkinson's disease Patients in Baghdad-Iraq. Methods: The sample consisted of 104 patients with Parkinson disease attended to the Neurosciences Hospital in Baghdad city / Iraq, aged 60-79 years Prosthetic Status was recorded according to WHO(1997). Weight status was recorded according to Trowbridge 1988 and occupation was recorded according to Erikson and Goldthorpe (1992) and Ganzeboom et al (

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Publication Date
Thu Dec 13 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Psychological Well-being for Elderly People at Geriatric Centers in Baghdad City
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Objective: To evaluate the levels of Psychological well-being among elderly people and To find out the relationship between socio-demographic characteristics and psychological well-being among elderly people who live in Geriatric centers. Methodology: A descriptive study in which evaluation approach is applied to achieve the objectives of the study the period of the study was from 29 December 2014 to 25 may 2015, The sample is non-probability (purposive sample) of 60 elderly people and selecte according to criteria of sample and for the purpose of the study , ( 40 ) are from Al Rashad and ( 20 ) Sleek

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Publication Date
Sat Oct 09 2021
Journal Name
Turkish Journal Of Computer And Mathematics Education
An Analysis of the Mathematics Book for the First Intermediate Grade According to the Dimensions of Mathematical Power
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The current research aims to analyze the mathematics book for the first intermediate grade according to the dimensions of mathematical power by answering the following question: What is the percentage of the availability of the dimensions of mathematical power included in the content of the mathematics textbook for first-grade students, average, in its first, and second parts for the academic year 2020-2021, approved by the Iraqi Ministry of Education / General Directorate of Curricula? The research community was determined by middle school mathematics books, and middle school students for middle ,and high school day schools affiliated to the Directorate of Education in Dhi Qar, and by the intentional test, the research sample was s

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Geological Journal
Study of Petrophysical Properties of the Yamama Formation in Siba Oilfield
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Evaluating a reservoir to looking for hydrocarbon bearing zones, by determining the petrophysical properties in two wells of the Yamama Formation in Siba field using Schlumberger Techlog software. Three porosity logs were used to identify lithology using MN and MID cross plots. Shale volume were calculated using gamma ray log in well Sb-6ST1 and corrected gamma ray in well Sb-5B. Sonic log was used to calculate porosity in bad hole intervals while from density log at in-gauge intervals. Moreover, water saturation was computed from the modified Simandoux equation and compared to the Archie equation. Finally, Permeability was estimated using a flow zone indicator. The results show that the Yamama Formation is found to be mainly limest

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Publication Date
Mon Oct 12 2026
Journal Name
Revista Iberoamericana De Psicología Del Ejercicio Y El Deporte, Issn 1886-8576, Vol. 16, Nº. 6, 2021, Págs. 9-13
The effect of Competitive Fitness (cross fit) exercises on the lactic ability and some biochemical variables for weight trainees
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Autorías: Amwag Mohammed Ali Qasim, Ghassan Adeeb Abdulhasan. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2021. Artículo de Revista en Dialnet.

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Publication Date
Tue Dec 21 2021
Journal Name
Mendel
Hybrid Deep Learning Model for Singing Voice Separation
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes On Data Engineering And Communications Technologies
Utilizing Deep Learning Technique for Arabic Image Captioning
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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Engineering
Asphaltene Precipitation Modeling of Sadi Formation in Halfaya Iraqi Oil Field
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Asphaltene is a component class that may precipitate from petroleum as a highly viscous and sticky material that is likely to cause deposition problems in a reservoir, in production well, transportation, and in process plants. It is more important to locate the asphaltene precipitation conditions (precipitation pressure and temperature) before the occurring problem of asphaltene deposition to prevent it and eliminate the burden of high treatment costs of this problem if it happens. There are different models which are used in this flow assurance problem (asphaltene precipitation and deposition problem) and these models depend on experimental testing of asphaltene properties. In this study, the used model was equation of

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Publication Date
Wed Aug 30 2023
Journal Name
Baghdad Science Journal
Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset
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The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

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