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Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
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Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parameters. A databases from one well drilled in carbonate environments were subjected to the predictive methods. Each raw dataset is described by eight parameters including rate of penetration (ROP), true vertical depth (TVD), weight on bit (WOB), bit rotational speed (RPM), torque (TQ), flow rate (Q), equivalent circulating density (ECD), standpipe pressure (SPP), and unconfined compressive strength (UCS). First, both MRA and ANNs are tested as predictive methods. The prediction capacity of each model was also verified by using two-based error metrics: the determination coefficient (R2) and the mean square error (MSE).</p><p>The current results support the evidence that MRA and ANNs are able to be effectively utilize the drilling data, and thus provide accurate ROP prediction. However, more attention to the multiple regression analysis is required where it is implemented for ROP prediction. ANNs appear to be more conservative in predicting ROP than MRA as indicated by a higher value R2 (0.96) and lower value MSE (1.89) of the ANN model. Considering the input parameters, the obtained results showed that TVD, WOB, RPM, SPP, and ECD had the greatest effect on estimated ROP-conditions, followed in decreasing by pump flow rate, drilling torque, and rock strength. Another important point that highlights in this study is that the drilling rate may increase with depth in carbonate rocks because of their heterogeneity. This study presents new models to estimate ROP from other parameters which can help the driller to achieve an optimal drilling rate through monitoring controllable parameters.</p>
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Publication Date
Mon May 15 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Solving System of Linear Fredholm Integral Equations of Second Kind Using Open Newton-Cotes Formulas
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In this paper, the linear system of Fredholm integral equations is solving using Open Newton-Cotes formula, which we use five different types of Open Newton-Cotes formula to solve this system.  Compare the results of suggested method with the results of another method (closed Newton-Cotes formula)    Finally, at the end of each method, algorithms and programs developed and written in MATLAB (version 7.0) and we give some numerical examples, illustrate suggested method

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Publication Date
Sun Oct 15 2023
Journal Name
Journal Of Yarmouk
Artificial Intelligence Techniques for Colon Cancer Detection: A Review
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Publication Date
Fri Aug 27 2021
Journal Name
Human Interaction, Emerging Technologies And Future Systems V: Proceedings Of The 5th International Virtual Conference On Human Interaction And Emerging Technologies, Ihiet 2021, August 27-29, 2021 And The 6th Ihiet: Future Systems (ihiet-fs 2021), October 28-30, 2021, France
Electricity Consumption Forecasting in Iraq with Artificial Neural Network
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Publication Date
Wed May 29 2019
Journal Name
Iraqi Journal Of Physics
Analysis the effect of economic conditions on the vegetative change in Baghdad using GIS and remote sensing techniques
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The study of vegetative change of cities is one of the most important studies related to human life because of its direct correlation with the temporal conditions that occur. These include the economic problems that force people to move and look for job opportunities in the city, which leads to an increase in the population density of cities, especially for cities with an important economic and administrative location as in the capital city of Baghdad. In this study, the effect of the increasing in population density was analyzed on the urban planning of Baghdad city. The decreasing in vegetation was due to the increasing of urban areas on the outskirts of the city, which led to an increase in its area. Moreover, urban cities increased t

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Publication Date
Wed May 10 2017
Journal Name
Journal Of The College Of Languages (jcl)
ФОРМИРОВАНИЕ КОММУНИКАТИВНО-РЕЧЕВОЙ КОМПЕТЕНЦИИ СТУДЕНТОВ КАФЕДРЫ Р The formation of verbal communication for students of Russian Language Department at College of Languages at University of Baghdad by using multiple technological means
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В статье рассматривается вопрос об использовании мультимедийных средств для оптимизации процесса формирования коммуникативной компетенции  в иракской аудитории с привлечением компьютерных технологий. Статья посвящена  использованию мультимедийных технологий  и  различных  приемов формирования интереса к русскому языку. Включение в процесс обучения коммуникативно-значимого, аутентичн

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Publication Date
Sat Jul 01 2023
Journal Name
Electric Power Systems Research
Analytical and measurement-based wideband two-port modeling of DC-DC converters for electromagnetic transient studies
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Power-electronic converters are essential elements for the effective interconnection of renewable energy sources to the power grid, as well as to include energy storage units, vehicle charging stations, microgrids, etc. Converter models that provide an accurate representation of their wideband operation and interconnection with other active and passive grid components and systems are necessary for reliable steady state and transient analyses during normal or abnormal grid operating conditions. This paper introduces two Laplace domain-based approaches to model buck and boost DC-DC converters for electromagnetic transient studies. The first approach is an analytical one, where the converter is represented by a two-port admittance model via mo

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Publication Date
Wed Apr 03 2024
Journal Name
International Journal Of Economics And Finance Studies
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ACCOUNTING PERFORMANCE: SUSTAINABLE DEVELOPMENT AS A MEDIATING VARIABLE
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The UN plans to achieve several development objectives by 2030. These objectives address global warming, a major issue. This method aims to improve sustainable accounting performance (AP). In this circumstance, AI is being applied in various fields, notably in economic, social, and environmental (ESE) domains. This research investigates how sustainable development (SD) influences AI methodologies and AP improvement. The research examined a sample of Iraqi banks listed on the Iraq Stock Exchange from 2014 to 2022. AI was measured by ATM and POS prevalence. A three-dimensional approach examined economic, social, and environmental (ESE) sustainability. Meanwhile, the performance of sustainable accounting was measured through the return on asse

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Publication Date
Wed Jan 15 2025
Journal Name
Journal Of Baghdad College Of Dentistry
Estimation of the linear dimensional changes of autoclave cured acrylic resin with multiple palatal depths and investment materials
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Background: Dimensional changes of acrylic denture bases after polymerization results in need for further adjustments or even ends with technical failure of the finished dentures. The purpose of this study was to estimate the linear dimensional changes for different palatal depths when using multiple investment materials and polymerization techniques. Materials and methods: Ninety upper complete denture bases were constructed for this study. They were divided into two main groups according to the polymerization methods: conventional water bath and experimental autoclave (short and long cycles). Each main group was further subdivided into three subgroups according to the palatal depth (shallow, medium and deep). Furthermore, for each palatal

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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
Mon Dec 02 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

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