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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
Thu Jun 01 2023
Journal Name
Baghdad Science Journal
Effective Computational Methods for Solving the Jeffery-Hamel Flow Problem
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In this paper, the effective computational method (ECM) based on the standard monomial polynomial has been implemented to solve the nonlinear Jeffery-Hamel flow problem. Moreover, novel effective computational methods have been developed and suggested in this study by suitable base functions, namely Chebyshev, Bernstein, Legendre, and Hermite polynomials. The utilization of the base functions converts the nonlinear problem to a nonlinear algebraic system of equations, which is then resolved using the Mathematica®12 program. The development of effective computational methods (D-ECM) has been applied to solve the nonlinear Jeffery-Hamel flow problem, then a comparison between the methods has been shown. Furthermore, the maximum

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Publication Date
Fri Dec 29 2023
Journal Name
International Journal Of Nanoscience
Detection of Silica in Rice Husks Using Laser-Induced Plasma and Studying the Effect of Laser Energy on the Parameters of the Produced Plasma
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This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed with

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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Harnessing Technological Innovation and Artificial Intelligence in Iraqi Commercial Banks to Achieve Sustainability
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Publication Date
Fri Aug 15 2025
Journal Name
Journal Port Science Research
The Antimicrobial Characteristics of Some Natural Remedies in Root Canal Disinfection and Artificial Intelligence (AI): A Review
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The primary goal of root canal treatment (RCT) is to expel the presence of any necrotic or vital tissue, microbes and their byproducts from the canal space before press forward with the following steps of the RCT procedures. Although this is difficult to attain, various strives had been practiced by employing chemical and mechanical methods to eliminate as much microorganisms as possible and make the canal space valid for the obturation materials to be received. The aim of this review is to demonstrate some of what new remedies that could be used as root canal disinfectant by summarizing the recent studies regarding the efficacy of different natural products against the most persistence microbiota that could be responsible for most

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Studying the Effects of Contamination on Soil Properties Using Remote Sensing
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The problem of soil contamination is increased recently due to increasing the industrial wastes such as petroleum hydrocarbon, organic solvents, and heavy metals as well as maximizing the use of agricultural fertilizers. During this period, wide development of data collection methods, using remote sensing techniques in the field of soil and environment applications appear and state the suitable technique for remediation. This study deals with the application of remote sensing techniques in geoenvironmental engineering through a field spectral reflectance measurements at nine spots of naturally hydrocarbons contaminated soil in Al-Daura Refinery Company site which is located to the south west of Baghdad using radiometer device to get stan

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Publication Date
Sun May 01 2016
Journal Name
Journal Of Engineering
Prediction of Ryznar Stability Index for Treated Water of WTPs Located on Al-Karakh Side of Baghdad City using Artificial Neural Network (ANN) Technique
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In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

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Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology & 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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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
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Publication Date
Sat Dec 01 2018
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The role of artificial intelligence in achieving Customer satisfaction and its reflection on cost accounting: An applied research in the Iraqi electronic industries company
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Based economic units to technology to add innovations that lead to contribute to customer satisfaction, under intense competition and rapid development in customer taste, the economic units tend to apply the concepts that contribute to customer satisfaction led by the introduction of artificial intelligence techniques. In the production prominent role in the contributing and responding to the rapid changes in customer tastes, and consequent impact this in achieving customer satisfaction. Search gained importance of relying on artificial intelligence techniques to achieve customer satisfaction through speed of response to changes in the tastes of customers and thus be able to increase its market share، and sales growth، and to achieve a

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Publication Date
Sun Aug 25 2019
Journal Name
Journal Of University Of Babylon For Engineering Sciences
Observation of Consolidation and Permeability Parameters of Soil Stabilized By Cutback Asphalt
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For design purposes, it`s necessary to know the compression rate of soil layers which might be happened when it`s subjected to effective stresses. Also, it`s essential to know the rate of flow through soil mass specially for the design of marine structures or earth embankment. These two important behavior could be predicted from the coefficient of consolidation (Cv) and the coefficient of permeability (k). This study shows the effect of cutback asphalt stabilization on Cv and k and other compressibility factors, the investigation was done for silty clay samples, specimens were prepared by mixing the soil with different percentage of asphalt from (0-10)% and subjected to one-dimensional consolidation test of 50mm diameter and 20mm height wer

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