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Perforation location optimization through 1-D mechanical earth model for high-pressure deep formations
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Abstract<p>Optimum perforation location selection is  an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magnitude of different stress types and geomechanical properties was estimated from well-log data using the Techlog software. The directions of the horizontal stresses are determined in the current wells utilizing image-log formation micro-imager (FMI) and caliper logs. The results in terms of rock mechanical properties showed a reduction in Poisson’s ratio, Young modulus, and bulk modulus near the high-pressure zones as compared to normal pressure zones because of the presence of anhydrite, salt cycles, and shales. Low maximum and minimum horizontal stress values are also observed in high-pressure zones as compared to normal pressure zones indicating the effects of geomechanical properties on horizontal stress estimation. Around the wellbore of the studied wells, formation breakouts are the most expected situation according to the results of the wellbore stress state (effective vertical stress (<italic>σ</italic><sub>zz</sub>) > effective tangential stress (<italic>σ</italic><sub>θθ</sub>) > effective radial stress (<italic>σ</italic><sub>rr</sub>)).</p>
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Publication Date
Fri Jan 01 2021
Journal Name
E3s Web Of Conferences
Effect of Halabjah Earthquake on Al-Wand Earth Dam: Numerical Analysis
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The Halabja earthquake occurred on 12/11/2017 in Iraq, with a magnitude of 7.3 Mw, which happened in the Iraqi-Iranian borders. This earthquake killed and injured many people in the Kurdish region in the north of the country. There is no natural disaster more dangerous than earthquake, especially it occurs without warning, great attention must be paid to the impact of earthquakes on the soil and preparing for a wave of earthquakes. Numerical modeling using specific elements is considered a powerful tool to investigate the required behavior of structures in Geotechnical engineering, and the main objective of this is to assess the response of the Al-Wand dam to the Halabja earthquake, as this dam is located in an area that has been su

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Publication Date
Sun Aug 30 2026
Journal Name
Journal Of Physical Education
Comparing Angiotensin Gene polymorphisms According to Mechanical Ability for Weightlifter Selection
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Publication Date
Wed Apr 05 2023
Journal Name
Journal Of Engineering
Construction Time-Cost Optimization Modeling Using Ant Colony Optimization
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In the field of construction project management, time and cost are the most important factors to be considered in planning every project, and their relationship is complex. The total cost for each project is the sum of the direct and indirect cost. Direct cost commonly represents labor, materials, equipment, etc.
Indirect cost generally represents overhead cost such as supervision, administration, consultants, and interests. Direct cost grows at an increasing rate as the project time is reduced from its original planned time. However, indirect cost continues for the life of the project and any reduction in project time means a reduction in indirect cost. Therefore, there is a trade-off between the time and cost for completing construc

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Publication Date
Sat Oct 31 2020
Journal Name
Eastern-european Journal Of Enterprise Technologies
Design and development of high-accuracy machine for wire bending
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Publication Date
Sun Jan 20 2019
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Synthesis, Antibacterial and Antifungal Activities for Novel Derivatives of 2,2'-(((1-benzylbenzoimidazol-2-yl)methyl)azanediyl)bis(ethan-1-ol)
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The compound 2,2'-(((1H-benzo(d)imidazol-2-yl)methyl)azanediyl)bis(ethan-1-ol) was reacted with benzyl bromide to afford compound (1) which used as row material to prepare a series of compounds through condensation reaction, the starting compound were reacted with tosyl chloride to protect the OH group  to afford compound 2, then reacted benzyl bromide to produce compound (2), then the compound (2) treated with three compounds ( 2-mercaptobenzthiazole, 2-mercaptobenimidazol and 2-chloromethyl benzimidazole) to form compounds 3a,b, 4a,b and 5a,b respectively. In the another step the click reaction of compound 2,2'-(((1H-benzo(d)imidazol-2-yl)methyl)azanediyl)bis(ethan-1-ol) with Propargyl bromide produce compound  6  which reacted

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Publication Date
Tue Jul 01 1997
Journal Name
Polymer-plastics Technology And Engineering
Reverse Calculation of Pressure from Pseudopressure
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Sun Aug 30 2026
Journal Name
International Journal Of Robotics And Control Systems
Integrating Multimodal Emotion Recognition with Deep Q-Learning for Adaptive Social Robot Interaction
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Publication Date
Thu May 02 2024
Journal Name
Petroleum And Coal
Wellbore Instability Analysis to Determine the Failure Criteria for Deep Well/H Oilfield
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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

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