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Wellbore Instability Analysis to Determine the Safe Mud Weight Window for Deep Well, Halfaya Oilfield
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Wellbore instability is one of the most common issues encountered during drilling operations. This problem becomes enormous when drilling deep wells that are passing through many different formations. The purpose of this study is to evaluate wellbore failure criteria by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict a safe mud-weight window for deep wells. An integrated log measurement has been used to compute MEM components for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results. The prediction of mud weight along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations ranges between 12.5 to 15 ppg. The predicted safe mud weight value seems to be narrow with a well deviation higher than 350. Therefore, for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations, the wellbore appears unstable compared to other formations. The results of stability analyses indicate that the breakout mud weight wasn’t affected by wellbore azimuth because of low-stress contrast. Furthermore, shear failure can be prevented by drilling the well with an inclination of less than 350. As well as, to prevent breakdown the well should be drilled with an inclination between 25o to 65o in the direction of minimum horizontal stress. These outcomes could be used to prevent wellbore instability and determine a safe mud-weight window when planning to drill nearby wells in the future.

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Publication Date
Sat Mar 01 1997
Journal Name
Polymer-plastics Technology And Engineering
Feasibility of Dynamic Acid Corrosion Control in Oil Well Tubing
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Publication Date
Sat Jan 01 2011
Journal Name
Iraqi Journal Of Physics
Single Particle Level Density in a Harmonic – Oscillator Potential Well
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The purpose of this paper is to study the properties of the
partial level density ( ) l g and the total level density g ( ),
numerically obtained as a l sum of ( ) l g up to 34 max l  , for
a Harmonic – Oscillator potential well. This method applied the
quantum – mechanical phase shift technique and concentrated
on the continuum region. Also a discussion of peculiarities of
quantal calculation for single particle level density of energy –
dependent potential

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Publication Date
Sun Sep 30 2018
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Torque and Drag Forces Problems in Highly Deviated Oil Well
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Excessive torque and drag can be critical limitation during drilling highly deviated oil wells. Using the modeling is regarded as an invaluable process to assist in well planning and to predict and prevent drilling problems. Identify which problems lead to excessive torque and drag to prevent cost losses and equipment damage. Proper modeling data is highly important for knowing and prediction hole problems may occur due to torque and drag and select the best method to avoid these problems related to well bore and drill string. In this study, Torque and drag well plan program from landmark worldwide programming group (Halliburton Company) used to identify hole problems.one deviated well in Zubair oil fields named, ZB-250 selected for

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Publication Date
Sun Jun 20 2021
Journal Name
Journal Of Petroleum Research And Studies
The Influence of Glass Fiber and Milled Glass Fiber on the Performance of Iraqi Oil Well Cement
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The reinforced fiberglass in cement slurry reflects the effect on its properties compared to usual additives. Fiberglass is typically used in cement slurry design for one or another of the following goals: (Earth earthquake, bearing storage, and with differential stresses, to enhance cement durability and increase its compressive strength). The main goal is to use glass fiber and ground fiberglass to improve the tensile strength and moderate compressive strength significantly. On the other hand, the use of glass fibers led to a slight increase in the value of thickening time, which is a desirable effect. Eleven glass fiber samples and milled glass fiber were used to show these materials' effect on Iraqi cement with (0.125, 0.25, 0.5

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Publication Date
Fri Jun 08 2018
Journal Name
Advances In Intelligent Systems And Computing
Improve Memory for Alzheimer Patient by Employing Mind Wave on Virtual Reality with Deep Learning
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Publication Date
Fri May 01 2026
Journal Name
Journal Of Engineering
Electronic Circuit Design and Modeling of Biodegradable Mxene-Based Wireless Biosensor for Deep Wound Monitoring
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One of the most significant challenges of medical care is the infection of postoperative wounds, and conventional visual examination often fails to detect it early. This research proposes the design of an innovative, passive wireless telemetry system for non-intrusive monitoring of the wound-healing process.  The system integrates a biocompatible resonance circuit (LC) with a high-sensitivity piezoresistive sensor based on MXene (Ti3C2Tx). It operates within the standard industrial and medical (ISM) band at 13.56 MHz.The detection mechanism in the system is based on the principle of "impedance modulation" (Impedance Modulation), which arises from changes in the sensor's resistance under physiological tissue pressure. The system was

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Publication Date
Wed Dec 01 2021
Journal Name
Computers & Electrical Engineering
Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
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Publication Date
Mon Jan 01 2024
Journal Name
Fusion: Practice And Applications
Proposed Framework for Semantic Segmentation of Aerial Hyperspectral Images Using Deep Learning and SVM Approach
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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Deep Learning-Driven Decision Fusion: Spatio-Spectrogram Features for Inner Speech Recognition From Electroencephalogram Signals
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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Human-machine Systems
Deep Learning-Driven Decision Fusion: Spatio-Spectrogram Features for Inner Speech Recognition From Electroencephalogram Signals
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