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Development of New Models to Determine the Rheological Parameters of Water-Based Drilling Fluid using Artificial Neural Networks
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It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works represent valid and reliable results, they are expensive and time consuming. In contrast, continuous and regular determination of the rheological fluid properties can perform its essential functions during good construction. The aim of this study is to develop empirical models to estimate the drilling mud rheological properties of water-based fluids with less need for lab measurements. This study provides two predictive techniques, multiple regression analysis and artificial neural networks, to determine the rheological properties of water-based drilling fluid using other simple measurable properties. While mud density, marsh funnel, and solid% are key input parameters in this study, the output models are plastic viscosity, yield point, apparent viscosity and gel strength. The prediction methods have been applied on datasets taken from the final reports of two wells drilled in the Ahdeb oil field, eastern Iraq. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error have been used in this study. The current results support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological mud properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties using simple and quick equipment such as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Cybersecurity And Information Management
A New Automated System Approach to Detect Digital Forensics using Natural Language Processing to Recommend Jobs and Courses
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A resume is the first impression between you and a potential employer. Therefore, the importance of a resume can never be underestimated. Selecting the right candidates for a job within a company can be a daunting task for recruiters when they have to review hundreds of resumes. To reduce time and effort, we can use NLTK and Natural Language Processing (NLP) techniques to extract essential data from a resume. NLTK is a free, open source, community-driven project and the leading platform for building Python programs to work with human language data. To select the best resume according to the company’s requirements, an algorithm such as KNN is used. To be selected from hundreds of resumes, your resume must be one of the best. Theref

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Publication Date
Tue Jun 14 2016
Journal Name
Transactions Of The Asabe
Using Terahertz Time-Domain Spectroscopy to Discriminate among Water Contamination Levels in Diesel Engine Oil
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Publication Date
Fri Apr 21 2023
Journal Name
Technologies And Materials For Renewable Energy, Environment And Sustainability: Tmrees22fr
Modified x-ray analysis size strain plot method to determine the lattice stress and strain energy density of calcium titan oxide (CaTiO3) nanoparticles
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In this research, the size strain plot method was used to estimate the particle size and lattice strain of CaTiO3 nanoparticles. The SSP method was developed to calculate new variables, namely stress, and strain energy, and the results were crystallite size (44.7181794 nm) lattice strain (0.001211), This method has been modified to calculate new variables such as stress and its value (184.3046308X10-3Mpa) and strain energy and its value (1.115833287X10-6 KJm-3).

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Publication Date
Tue Dec 21 2021
Journal Name
Journal Of Engineering Mathematics
Fluid inflow from a source on the base of a channel
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Publication Date
Sun Jun 30 2002
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
The Convective Heat Transfer of Fluid Flowing Across a Vertical Plate
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Publication Date
Tue Jan 02 2024
Journal Name
Advances In The Theory Of Nonlinear Analysis And Its Application
Properties of Capturing of Peristaltic Flow to A Chemically Reacting Couple Stress Fluid Through an Inclined Asymmetric Channel with Variable Viscosity and Various Boundaries
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The properties of capturing of peristaltic flow to a chemically reacting couple stress fluid through an inclined asymmetric channel with variable viscosity and various boundaries are investigated. we have addressed the impacts of variable viscosity, different wave forms, porous medium, heat and mass transfer for peristaltic transport of hydro magnetic couple stress liquid in inclined asymmetric channel with different boundaries. Moreover, The Fluid viscosity assumed to vary as an exponential function of temperature. Effects of almost flow parameters are studied analytically and computed. An rising in the temperature and concentration profiles return to heat and mass transfer Biot numbers. Noteworthy, the Soret and Dufour number effect resul

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
The effect of using magnetized water on the percentage of cement in the Concrete mixture
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This research studied the effect of magnetized water in concrete preparation and its effect on the presenting of cement in concrete mixtures also to find the ability of reducing the amount of cement in preparing one cubic meter, this is not exceed than 10% in one mixture , The experiments showed the preparation of standard cubes from the concrete which was used two kind of water magnetized water which was prepared by passing the tap water through the systems of different magnetic strength in terms of (6000,9000) Gauss and the ordinary water . The velocity of water through the magnetic field, which gives us the highest value for the compressive strength, was up to 1m/sec. to determine the best magnetic intensity, we examined The comp

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Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of International Conference On Computing And Communication Networks
Speech Age Estimation Using a Ranking Convolutional Neural Network
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Publication Date
Tue Dec 31 2024
Journal Name
Journal Of Soft Computing And Computer Applications
Enhancing Image Classification Using a Convolutional Neural Network Model
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In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

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Publication Date
Wed Apr 24 2019
Journal Name
Annals Of Telecommunications
Traveling distance estimation to mitigate unnecessary handoff in mobile wireless networks
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