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Studying the Effect of Permeability Prediction on Reservoir History Matching by Using Artificial Intelligence and Flow Zone Indicator Methods
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The map of permeability distribution in the reservoirs is considered one of the most essential steps of the geologic model building due to its governing the fluid flow through the reservoir which makes it the most influential parameter on the history matching than other parameters. For that, it is the most petrophysical properties that are tuned during the history matching. Unfortunately, the prediction of the relationship between static petrophysics (porosity) and dynamic petrophysics (permeability) from conventional wells logs has a sophisticated problem to solve by conventional statistical methods for heterogeneous formations. For that, this paper examines the ability and performance of the artificial intelligence method in permeability prediction and compared its results with the flow zone indicator methods for a carbonate heterogeneous Iraqi formation. The methodology of the research can be Summarized by permeability was estimated by using two methods: Flow zone indicator and Artificial intelligence, two reservoir models are built, where the difference between them is in permeability method estimation, and the simulation run will be conducted on both of the models, and the permeability estimation methods will be examined by comparing their effect on the model history matching. The results showed that the model with permeability predicted by using artificial intelligence matched the observed data for different reservoir responses more accurately than the model with permeability predicted by the flow zone indicator method. That conclusion is represented by good matching between observed data and simulated results for all reservoir responses such for the artificial intelligence model than the flow zone indicator model.

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Publication Date
Fri Sep 20 2019
Journal Name
Journal Of The College Of Education For Women
History of Local Reprimands in the Northwest Caucasus from Ancient Times to the 19th century
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Ubikh is one of the most important tribes of the Northwest Caucasus and has a long history. Naturally, rebukes are an integral part of the Caucasus region. Their people were able to confront the Russian invasion of their lands like the rest of the Caucasus tribes to defend their existence, freedom and the right to build their independent state, but they were not able to resist the great Russian military progress they achieved on all sides. For the territory that belonged to Persia and the Ottoman Empire and ended the resistance of the Meridian movement in the Northeast Caucasus, the Russians left only the weak resistance of the tribes of the Northwest Caucasus led by the rebuke after Russia managed to eliminate all resistance movements i

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Publication Date
Sun Mar 09 2008
Journal Name
Um-salama Science Journal
Studying of the complexes product of the nerve agent Soman with the Butyrylcholinesterase and Acetylcholinesterase Enzymes
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Cholinesterases are among the most efficient enzymes known. They are divided into two groups: acetylcholinesterase (AChE) involved in the hydrolysis of the neurotransimitter acetylcholine, and butyrylcholinesterase (BChE) of unknown function. Several crystal structures of the former have shown that the active site is located at the bottom of a deep and narrow gorge. Human BChE has attracted attention because it can hydrolyze toxic esters and nerve agents. Here we analyze the complexes of cholinesterase with soman by describing the 3D geometry of the complex, the active site, the changes happened through the inhibition and provide a description for the mechanism of inhibition. Soman undergoes degradation in the active site of the AChE and BC

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Publication Date
Sun Jun 01 2008
Journal Name
Baghdad Science Journal
Studying of the complexes product of the nerve agent Soman with the Butyrylcholinesterase and Acetylcholinesterase Enzymes
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Cholinesterases are among the most efficient enzymes known. They are divided into two groups: acetylcholinesterase (AChE) involved in the hydrolysis of the neurotransimitter acetylcholine, and butyrylcholinesterase (BChE) of unknown function. Several crystal structures of the former have shown that the active site is located at the bottom of a deep and narrow gorge. Human BChE has attracted attention because it can hydrolyze toxic esters and nerve agents. Here we analyze the complexes of cholinesterase with soman by describing the 3D geometry of the complex, the active site, the changes happened through the inhibition and provide a description for the mechanism of inhibition. Soman undergoes degradation in the active site of the AChE and B

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region
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Publication Date
Tue Mar 01 2016
Journal Name
Journal Of Engineering
Prediction of Raw Water Turbidity at the Intakes of the Water Treatment Plants along Tigris River in Baghdad, Iraq using Frequency Analysis
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Different frequency distributions models were fitted to the monthly data of raw water Turbidity at water treatment plants (WTPs) along Tigris River in Baghdad. Eight water treatment plants in Baghdad were selected, with raw water turbidity data for the period (2008-2014). The frequency distribution models used in this study are the Normal, Log-normal, Weibull, Exponential and two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit. The data for years (2008-2011) were used for building the models. The best fitted distributions were Log-Normal (LN) for Al-Karkh, Al-Wathbah, Al-Qadisiya, Al- Dawrah and, Al-Rashid WTPs. Gamma distribution fitted well for East Tigris and Al-Karamah WTPs. As for Al-

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Publication Date
Tue Mar 01 2016
Journal Name
Journal Of Engineering
Prediction of Raw Water Turbidity at the Intakes of the Water Treatment Plants along Tigris River in Baghdad, Iraq using Frequency Analysis
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Different frequency distributions models were fitted to the monthly data of raw water Turbidity at water treatment plants (WTPs) along Tigris River in Baghdad. Eight water treatment plants in Baghdad were selected, with raw water turbidity data for the period (2008-2014). The frequency distribution models used in this study are the Normal, Log-normal, Weibull, Exponential and two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit.  The data for years (2008-2011) were used for building the models. The best fitted distributions were Log-Normal (LN) for Al-Karkh, Al-Wathbah, Al-Qadisiya, Al-Dawrah and, Al-Rashid WTPs. Gamma distribution fitted well for East Tigris and Al-Karamah

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of Two Educational Models in Acquiring Historical Concepts and Orientation of Fourth Grade Students in History
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The study focuses on Kamez model and the Claus Meyer model of instructional design, which are models that provide the learner with educational experiences to suit the logical information of the learner and the variety of instructional models. Research Objective: The present research aims to identify Limitations of the study. The current research is determined by ((fourth grade preparatory students, the book of the date of the fourth preparatory course)) Chapter II includes Arabic and foreign studies on the model of Kemp and Claus Mayer in the acquisition of concepts and direction towards the material. Chapter III Experimental Design: The researcher adopted an experimental design with two experimental groups and a control group. The resea

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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Thu Jun 01 2023
Journal Name
Journal Of Engineering
Fault Location of Doukan-Erbil 132kv Double Transmission Lines Using Artificial Neural Network ANN
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

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Publication Date
Sat Jan 01 2022
Journal Name
International Middle Eastern Simulation And Modelling Conference 2022, Mesm 2022,
MECHANICS OF COMPOSITE PLATE STRUCTURE REINFORCED WITH HYBRID NANO MATERIALS USING ARTIFICIAL NEURAL NETWORK
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