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Permeability Prediction in One of Iraqi Carbonate Reservoir Using Hydraulic Flow Units and Neural Networks
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Permeability determination in Carbonate reservoir is a complex problem, due to their capability to be tight and heterogeneous, also core samples are usually only available for few wells therefore predicting permeability with low cost and reliable accuracy is an important issue, for this reason permeability predictive models become very desirable.

   This paper will try to develop the permeability predictive model for one of  Iraqi carbonate reservoir from core and well log data using the principle of Hydraulic Flow Units (HFUs). HFU is a function of Flow Zone Indicator (FZI) which is a good parameter to determine (HFUs).

   Histogram analysis, probability analysis and Log-Log plot of Reservoir Quality Index (RQI) versus normalized porosity (øz) are presented to identify optimal hydraulic flow units. Four HFUs were distinguished in this study area with good correlation coefficient for each HFU (R2=0.99), therefore permeability can be predicted from porosity accurately if rock type is known.

   Conventional core analysis and well log data were obtained in well 1 and 2 in one of carbonate Iraqi oil field. The relationship between core and well log data was determined by Artificial Neural Network (ANN) in cored wells to develop the predictive model and then was used to develop the flow units prediction to un-cored wells. Finally permeability can be calculated in each HFU using effective porosity and mean FZI in these HFUs. Validation of the models evaluated in a separate cored well (Blind-Test) which exists in the same formation. The results showed that permeability prediction from ANN and HFU matched well with the measured permeability from core data with R2 =0.94 and ARE= 1.04%.

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Publication Date
Fri Nov 11 2011
Journal Name
Thesis
Performance Analysis of a New Compact Magneto-Rheological Proportional Control Valve for Hydraulic Actuation Using FEM and Experimental Approach
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One of the main parts in hydraulic system is directional control valve, which is needed in order to operate hydraulic actuator. Practically, a conventional directional control valve has complex construction and moving parts, such as spool. Alternatively, a proposed Magneto-rheological (MR) directional control valve can offer a better solution without any moving parts by means of MR fluid. MR fluid consists of stable suspension of micro-sized magnetic particles dispersed in carrier medium like hydrocarbon oil. The main objectives of this present research are to design a MR directional control valve using MR fluid, to analyse its magnetic circuit using FEMM software, and to study and simulate the performance of this valve. In this research, a

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Engineering
New Correlation for Predicting Undersaturated Oil Compressibility for Mishrif Reservoir in the Southern Iraqi Oil Fields
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Reservoir fluids properties are very important in reservoir engineering computations such as material balance calculations, well testing analyses, reserve estimates, and numerical reservoir simulations. Isothermal oil compressibility is required in fluid flow problems, extension of fluid properties from values at the bubble point pressure to higher pressures of interest and in material balance calculations (Ramey, Spivey, and McCain). Isothermal oil compressibility is a measure of the fractional change in volume as pressure is changed at constant temperature (McCain). The most accurate method for determining the Isothermal oil compressibility is a laboratory PVT analysis; however, the evaluation of exploratory wells often require an esti

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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Prediction of consolidation due to dewatering by using MATLAB software
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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Prediction of COVID 19 Disease Using Feature Selection Techniques
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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in </p> ... Show More
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Publication Date
Mon Dec 18 2017
Journal Name
Al-khwarizmi Engineering Journal
Optimization and Prediction of Process Parameters in SPIF that Affecting on Surface Quality Using Simulated Annealing Algorithm
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Incremental sheet metal forming is a modern technique of sheet metal forming in which a uniform sheet is locally deformed during the progressive action of a forming tool. The tool movement is governed by a CNC milling machine. The tool locally deforms by this way the sheet with pure deformation stretching. In SPIF process, the research is concentrate on the development of predict models for estimate the product quality. Using simulated annealing algorithm (SAA), Surface quality in SPIF has been modeled. In the development of this predictive model, spindle speed, feed rate and step depth have been considered as model parameters. Maximum peak height (Rz) and Arithmetic mean surface roughness (Ra) are used as response parameter to assess th

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Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
Lossy Image Compression Using Wavelet Transform, Polynomial Prediction And Block Truncation Coding
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Publication Date
Tue Jan 17 2017
Journal Name
International Journal Of Science And Research (ijsr)
Detection System of Varicose Disease using Probabilistic Neural Network
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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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Publication Date
Tue Mar 29 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
Prediction of bearing capacity of driven piles for Basrah governatore using SPT and MATLAB
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Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

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Publication Date
Sun Apr 08 2018
Journal Name
Al-khwarizmi Engineering Journal
Energy Consumption Analyzing in Single hop Transmission and Multi-hop Transmission for using Wireless Sensor Networks
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Wireless sensor networks (WSNs) are emerging in various application like military, area monitoring, health monitoring, industry monitoring and many more. The challenges of the successful WSN application are the energy consumption problem. since the small, portable batteries integrated into the sensor chips cannot be re-charged easily from an economical point of view. This work focusses on prolonging the network lifetime of WSNs by reducing and balancing energy consumption during routing process from hop number point of view. In this paper, performance simulation was done between two types of protocols LEACH that uses single hop path and MODLEACH that uses multi hop path by using Intel Care i3 CPU (2.13GHz) laptop with MATLAB (R2014a). Th

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