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ijs-12165
The Role of (Geoelectric and Hydrogeologic) Parameters in the Evaluation of Groundwater reservoir at South of Jabal Sinjar area.
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In this study the (geoelectric – hydrogeologic) parameters which are obtained by the
quantitative interpretation of (80) Schlumberger Vertical Electrical Sounding (VES)
points distributed in six linear profiles within the study area are used in addition to
(6) pumping test locations for the groundwater reservoir located to the south of Jabal
Sinjar (Sinjar anticline). The studied area covers about 7920Km2. The (VES) field
readings were interpreted manually by using the auxiliary point method-partial
resistivity curve matching,then the interpreted results enhanced by using computer
software specialized for the 1D- (VES) resistivity curves interpretation. The (VES)
results analyzed by using modern techniques in order to construct a new predicted
hydrogeologic maps through the application of an empirical statistical relations
between geoelectric and the Hydraulic parameters. The results of empirical relations
represent the predicted hydraulic parameters for the points where no pumping tests
achieved. The results represents the predicted hydraulic conductivity (K),
Transmissivity(Tr), Specific capacity(Sc) and Total Dissolved Solids (TDS). A
computer software used to display the results as maps to display the calculated
hydrogeologic parameters variation across the studied area. This result helps to
delineate the most productive and good quality groundwater within the study area.

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
User Oriented Calibration Method for Stonex X300 Terrestrial Laser Scanner
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    Terrestrial laser scanners (TLSs) are 3D imaging systems that provide the most powerful 3D representation and practical solutions for various applications. Hence this is due to effective range measurements, 3D point cloud reliability, and rapid acquisition performance. Stonex X300 TOF scanner delivered better certainty in far-range than in close-range measurements due to the high noise level inherent within the data delivered from Time of Flight (TOF) scanning sensors. However, if these errors are manipulated properly using a valid calibration model, more accurate products can be obtained even from very close-range measurements. Therefore, to fill this gap, this research presents a user-oriented target-based calibration routine to

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Publication Date
Sun Jun 23 2019
Journal Name
Arma
Safe Mud Weight Window Determination: A Case Study from Southern Iraq
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ABSTRACT:. The Lower Cretaceous Zubair formation is comprised of sandstones intercalated with shale sequences. The main challenges that were encountered while drilling into this formation included severe wellbore instability-related issues across the weaker formations overlaying the reservoir section (pay zone). These issues have a significant impact on well costs and timeline. In this paper, a comprehensive geomechanical study was carried out to understand the causes of the wellbore failure and to improve drilling design and drilling performance on further development wells in the field. Failure criteria known as Mogi-Coulomb was used to determine an operating mud weight window required for safe drilling. The accuracy of the geomechanical

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Publication Date
Tue Feb 28 2023
Journal Name
Iraqi Journal Of Science
Benchmarking Framework for COVID-19 Classification Machine Learning Method Based on Fuzzy Decision by Opinion Score Method
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     Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

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