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Optimizing Well Placement with Genetic Algorithms: A Case Study
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Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a type of stochastic optimization technique that has proven effective in solving various problems. The results of the study show significant improvements in NPV when using genetic algorithms compared to traditional methods, particularly for problems with numerous decision variables. The findings suggest that genetic algorithms are a promising tool for optimizing well placement in oil field development, improving NPV, and reducing the risk of project failure.

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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Engineering
Optimization of Locations for Bioswales Stormwater Management Using BMP Siting Tool - Case Study of Sulaymaniyah City-KRG-Iraq
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Today, urban Stormwater management is one of the main concerns of municipalities and stakeholders. Drought and water scarcity made rainwater harvesting one of the main steps toward climate change adaptation. Due to the deterioration of the quality of urban runoff and the increase of impermeable urban land use, the treatment of urban runoff is essential. Best Management Practice (BMP) and Low Impact Development (LID) approaches are necessary to combat climate change consequences by improving the quantity and quality of water resources. The application of Bioswales along urban streets and roadways can reduce the stress on water resources, recharge groundwater and prevent groundwater pollution. While Sulaymaniyah City has a

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Publication Date
Mon Oct 01 2012
Journal Name
Al–bahith Al–a'alami
Issues of Iraqi Photojournalists Working in Local and Foreign Institutions in Iraq: Iraqi Photojournalists Association - Case Study 2012
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The issues of journalists and media employees in general and photojournalists in particular have become important issues, especially as those issues are closely linked to the success or failure of the media process.

This research deals with (the issues of Iraqi photojournalists working in local and foreign institutions in Iraq - a case study in 2012), because of the ambiguity in identifying those issues, which focused on the issues of this research.

This was done through the research community of members of the Association of Iraqi photojournalists in Baghdad exclusively of (64) photographers and television photographers to identify the problems encountered in their work

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Publication Date
Sun Jan 01 2012
Journal Name
Journal Of The College Of Languages (jcl)
A Phonological Study of English and Arabic Assimilation : A Contrastive Study
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        Assimilation is defined ,by many phoneticians like  Schane ,Roach ,and many others, as a phonological process when there is a change of one sound into another because of  neighboring sounds.This study investigates the  phoneme assimilation as a phonological process in English and Arabic  and it is concerned specifically with the differences and similarities in both languages.   Actually ,this study reflects the different terms which are used  in Arabic to refer to this phenomenon and in this way it  shows whether the term 'assimilation ' can have the same meaning of  'idgham' in Arabic or not . Besides, in Arabic , this phenomenon is discussed from&nb

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Publication Date
Mon Sep 01 2025
Journal Name
International Communications In Heat And Mass Transfer
Optimizing the shell-and-multitube latent heat thermal storage: Influence of tube layout and fin design on consecutive/simultaneous operation modes
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Publication Date
Thu Mar 01 2018
Journal Name
2018 International Conference On Computing Sciences And Engineering (iccse)
Comparison between Epsilon Normalized Least Means Square (ϵ-NLMS) and Recursive Least Squares (RLS) Adaptive Algorithms
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There is an evidence that channel estimation in communication systems plays a crucial issue in recovering the transmitted data. In recent years, there has been an increasing interest to solve problems due to channel estimation and equalization especially when the channel impulse response is fast time varying Rician fading distribution that means channel impulse response change rapidly. Therefore, there must be an optimal channel estimation and equalization to recover transmitted data. However. this paper attempt to compare epsilon normalized least mean square (ε-NLMS) and recursive least squares (RLS) algorithms by computing their performance ability to track multiple fast time varying Rician fading channel with different values of Doppler

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Publication Date
Fri Sep 26 2025
Journal Name
Applied Data Science And Analysis
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

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Publication Date
Tue Jun 01 2010
Journal Name
Al-khwarizmi Engineering Journal
Comparison of the RLS and LMS Algorithms to Remove Power Line Interference Noise from ECG Signal
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    Biomedical signal such as ECG is extremely important in the diagnosis of patients and is commonly recorded with a noise. Many different kinds of noise exist in biomedical environment such as Power Line Interference Noise (PLIN). Adaptive filtering is selected to contend with these defects, the adaptive filters can adjust the filter coefficient with the given filter order. The objectives of this paper are: first an application of the Least Mean Square (LMS) algorithm, Second is an application of the Recursive Least Square (RLS) algorithm to remove the PLIN. The LMS and RLS algorithms of the adaptive filter were proposed to adapt the filter order and the filter coefficients simultaneously, the performance of existing LMS

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Publication Date
Thu Jan 01 2026
Journal Name
Sustainable Civil Infrastructures
Using Artificial Intelligence Algorithms to Predict the Physical Fitness Level in Iraqi Premier League Football Players
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Publication Date
Sun Mar 01 2015
Journal Name
Journal Of Engineering
Multi-Sites Multi-Variables Forecasting Model for Hydrological Data using Genetic Algorithm Modeling
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A two time step stochastic multi-variables multi-sites hydrological data forecasting model was developed and verified using a case study. The philosophy of this model is to use the cross-variables correlations, cross-sites correlations and the two steps time lag correlations simultaneously, for estimating the parameters of the model which then are modified using the mutation process of the genetic algorithm optimization model. The objective function that to be minimized is the Akiake test value. The case study is of four variables and three sites. The variables are the monthly air temperature, humidity, precipitation, and evaporation; the sites are Sulaimania, Chwarta, and Penjwin, which are located north Iraq. The model performance was

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Publication Date
Tue May 01 2012
Journal Name
2012 Second International Conference On Digital Information And Communication Technology And It's Applications (dictap)
The compact Genetic Algorithm for likelihood estimator of first order moving average model
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Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results

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