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Modeling of Oil Viscosity for Southern Iraqi Reservoirs using Neural Network Method
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The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperature andpressure extends from the reservoir to surface conditions.The generated viscosity data is utilized in the neural network tool (NN) to get fittingmodel correlates the viscosity of oil with composition, pressure and temperature. Theresulted error and the correlation coefficient of the model constructed are close to 0and 1 respectively. The NN model is also tested with data that are not used in set upthe model. The results proved the validity of the model. Moreover, the model’soutcomes demonstrate its superiority to selected empirical correlations.

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Publication Date
Mon Mar 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
Purification and activation of the Iraqi bentonite for edible oil Production
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Abstract<p>Samples of Iraqi bentonitic sediments, representing local montmorillonite brought from Traifawi region near the Syrian border. Mineralogical the samples were characterized as low grade of Ca-smectite, particle size, chemical analysis, XRD, and BET surface area analyses of the samples were carried out to examine the structure of bentonite before and after acid activation. The goal is to prepare a bleaching earth for edible oil production. Iraqi Bentonite was beneficiated and activated by series of physical and chemical steps, using 4N & 6N concentration of hydrochloric acid and at a temperature of 70-80 ° C. Surface area and pore volume of the samples were determined to assess the bleaching power</p> ... Show More
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Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
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         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

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Publication Date
Fri Jan 01 2016
Journal Name
Computational Intelligence And Neuroscience
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)
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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Engineering
Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq
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The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

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Publication Date
Wed Jun 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Compared with Genetic Algorithm Fast – MCD – Nested Extension and Neural Network Multilayer Back propagation
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The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

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Publication Date
Wed Feb 22 2023
Journal Name
Iraqi Journal Of Science
Paleoenvironments and Sequence Stratigraphy of the Turonian-Lower Campanian Succession at Majnoon Oil Field, Southern Iraq
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The Turonian-Lower Companian succession at Majnoon Oil Field is represented by the Khasib, Tanuma, and Saadi formations. Four major paleoenvironments were recognized within the studied succession, there are: Shallow open marine environment, shoal environment, deep marine environment, and basinal environment. They reflect deposition on a carbonate platform of homoclinal ramp setting. The studied succession represents two second order supersequences (A) and (B). Supersequence (A) includes both the Khasib and Tanuma formations. The Saadi Formation represents cycle (B). These second order cycles can be divided each into two third order cycles, This subdivision may reflect the effect of eustacy being the major controlling factor of cycles dev

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Publication Date
Mon Dec 23 2019
Journal Name
Modeling Earth Systems And Environment
Facies architecture and stratigraphic sequence of Zubair Formation in Majnoon and Suba oil fields, Southern Iraq
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Publication Date
Mon Apr 22 2019
Journal Name
Spe
Evaluation of Gas and Downhole Water Sink-Assisted Gravity Drainage GDWS-AGD Process in Saturated Oil Reservoirs with Infinite-Acting Aquifer
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Abstract<p>A hybrid Gas-Enhanced and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) process has been suggested to enhance oil recovery by placing vertical injectors for CO2 at the top of the reservoir with a series of horizontal oil-producing and water-drainage wells located above and below the oil-water contact, respectively. The injected gas builds a gas cap that drives the oil to the (upper) oil-producing wells while the bottom water-drainage wells control water cresting. The hybrid process of GDWS-AGD process has been first developed and tested in vertical wells to minimize water cut in reservoirs with bottom water drive and strong water coning tendencies. The wells were dual-compl</p> ... Show More
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Publication Date
Thu Sep 01 2022
Journal Name
Fuel
Experimental evaluation of Carbon Dioxide-Assisted Gravity Drainage process (CO2-AGD) to improve oil recovery in reservoirs with strong water drive
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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Influence of Nanosilica on Solvent Deasphalting for Upgrading Iraqi Heavy Crude Oil
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In this study, the upgrading of Iraqi heavy crude oil was achieved utilizing the solvent deasphalting approach (SDA) and enhanced solvent deasphalting (e-SDA) by adding Nanosilica (NS). The NS was synthesized from local sand. The XRD result, referred to as the amorphous phase, has a wide peak at 2Θ= (22 - 23º) The inclusion of hydrogen-bonded silanol groups (Si–O–H) and siloxane groups (Si–O–Si) in the FTIR spectra. The SDA process was handled using n-pentane solvent at various solvent to oil ratios (SOR) (4-16/1ml/g), room and reflux temperature, and 0.5 h mixing time. In the e-SDA process, various fractions of the NS (1–7 wt.%) have been utilized with 61 nm particle size and 560.86 m²/g surface area in the presence of 12 m

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