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Distribution of New Horizontal Wells by the Use of Artificial Neural Network Algorithm
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Abstract<p>It is an established fact that substantial amounts of oil usually remain in a reservoir after primary and secondary processes. Therefore; there is an ongoing effort to sweep that remaining oil. Field optimization includes many techniques. Horizontal wells are one of the most motivating factors for field optimization. The selection of new horizontal wells must be accompanied with the right selection of the well locations. However, modeling horizontal well locations by a trial and error method is a time consuming method. Therefore; a method of Artificial Neural Network (ANN) has been employed which helps to predict the optimum performance via proposed new wells locations by incorporating reservoir properties and production data of previous wells.</p><p>This study used the Artificial Neural Network (ANN) that has been programmed in a manner to predict the cumulative oil produced for a certain grid by providing the corresponding properties of the grid. The network has been validated with real data collected from a number of drilled hypothetical wells. Furthermore; the validated network used to simulate the field parts that have not been drilled yet, to predict the corresponding cumulative oil for each grid. Field-scale simulation has been carried out and new horizontal wells have been allocated using the validated prepared data by the Artificial Neural Network Algorithm and an approved Iraqi reservoir model. Finally, different optimization scenarios have been investigated on the overall field recovery performance.</p>
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Publication Date
Mon Feb 13 2023
Journal Name
Journal Of Educational And Psychological Researches
The Degree of Practicing Citizenship Values by Bisha University Students
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Abstract

 

The study aims to identify the degree of citizenship values practiced by Bisha University students and identify the impact of gender, college, and academic level, on the degree of the practice of University students for citizenship values. The researcher used the descriptive-analytical method including a questionnaire of (44) items. To process the data, the researcher applied the computational averages, standard deviations, percentages, and T-test. The questionnaire was implemented on a sample of (600) of the 2 and 8 levels during the second semester of the academic year (2020-2019) at Bisha University. The study findings revealed that the degree of the pra

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Publication Date
Sat Mar 30 2013
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Studying the Performance of Refrigeration Units Powered by Solar Panel
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An experimental study was conducted to determine the performance of a solar electric refrigeration system. The system contained flat photovoltaic solar panel which absorbs the solar energy and convert it to electrical energy, used to run the refrigeration cycle. Two refrigeration cycles with electrical solar panel were used over a period of 12 months, the first one with classical parts known in refrigeration cycle, while the second one introduced heat exchanger which improves the coefficient of performance by saving the consumed energy. The coefficient of performance of these refrigeration cycles with compressor efficiency 85% are 2.102 and 2.57 respectively. The overall efficiency of the two systems are 18.9% and 23.13%.

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Publication Date
Fri Apr 05 2013
Journal Name
Journal Of Intelligent Material Systems And Structures
Increasing the power of piezoelectric energy harvesters by magnetic stiffening
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A piezoelectric cantilever beam with a tip mass at its free end is a common energy harvester configuration. This article introduces a new principle of designing such a harvester that increases the generated power without changing the resonance frequency of the harvester: the attraction force between two permanent magnets is used to add stiffness to the system. This magnetic stiffening counters the effect of the tip mass on the efficient operation frequency. Five set-ups incorporating piezoelectric bimorph cantilevers of the same type in different mechanical configurations are compared theoretically and experimentally to investigate the feasibility of this principle: theoretical and experimental results show that magnetically stiffened harve

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Publication Date
Sun Dec 01 2024
Journal Name
Anbar Journal Of Agricultural Sciences
IMPROVING THE QUALITY OF POTATO MINI TUBERS BY SUSTAINABLE CULTIVATION
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Publication Date
Fri Jun 20 2025
Journal Name
Journal Of Kufa For Chemical Sciences
Synthesis, Spectroscopy characterization, theoretical study and biological activity of some new metal complexes with new Schiff base ligand derived from cefixime
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A set newly complexes with the general formula [M(L)Cl2] are resulting from the reaction of a new schiff base ligand [Ethyl (6R,7R)-7-((E)-2-((2-ethoxy-2- oxoethoxy)imino)-2-(2-(((E)-4-nitrobenzylidene) amino) thiazol -4- yl) acetamido) -8- oxo -3- vinyl -5- thia -1-aza bicyclo [4. 2.0] oct -2- ene -2- carboxylate] (L). This ligand was derived from the reaction of the two substances 4-nitrobenzaldehyde and precursor (P). Reaction the ligand with metal ions M= Mn(II), Co(II), Ni(II), Cu(II) and Cd(II) afforded new complexes which are characterized by FT-IR and Electronic Spectra. These measurements indicate that the complexes have a tetrahedral geometry. The Penicillin-Binding Protein 3 (PBP3) of Staphylococcus aureus and the target protein

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Publication Date
Tue Apr 02 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

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Publication Date
Thu Jan 01 2026
Journal Name
Lecture Notes In Networks And Systems
Using Artificial Intelligence for Optimizing Electronic Waste Recycling Processes in Sustainable Green Processing Chains
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Publication Date
Thu May 07 2026
Journal Name
Frontiers In Sustainability
Using ethical artificial intelligence (EAI) to achieve sustainable development, Iraq as a case study
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Introduction

Iraq faces persistent challenges in achieving sustainable development due to decades of conflict, political instability, and infrastructural degradation. These challenges are particularly evident in critical sectors such as energy, water, healthcare, education, and governance, which significantly influence human well-being, social equity, and quality of life. This study proposes an AI-driven, ethically guided, and human-centric sustainability framework to support resilient urban transformation in Iraq.

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Publication Date
Mon May 31 2021
Journal Name
Journal Of Research In Medical And Dental Science
A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique
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A Stereomicroscopic Evaluation of Four Endodontic Sealers Penetration into Artificial Lateral Canals Using Gutta-Percha Single Cone Obturation Technique, Omar Jihad Banawi*, Raghad

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Publication Date
Thu Jul 02 2026
Journal Name
Journal Of Engineering
A Hybrid Artificial Intelligence Model for Predicting Quality of Service (QoS) in 5G Networks Based on Wireless Channel Parameters
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This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

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