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A Survey of Infill Well Location Optimization Techniques
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The maximization of the net present value of the investment in oil field improvements is greatly aided by the optimization of well location, which plays a significant role in the production of oil. However, using of optimization methods in well placement developments is exceedingly difficult since the well placement optimization scenario involves a large number of choice variables, objective functions, and restrictions. In addition, a wide variety of computational approaches, both traditional and unconventional, have been applied in order to maximize the efficiency of well installation operations. This research demonstrates how optimization approaches used in well placement have progressed since the last time they were examined. Following that, the research looked at a variety of different optimization strategies, and it demonstrated the limitations of each strategy as well as the scope of its application in order to achieve a suitable level of accuracy and simulation run time. In conclusion, this study presents an all-encompassing analysis of the well location optimization approaches that are applied in the petroleum engineering area, ranging from traditional methods to contemporary methods that make use of artificial intelligence.

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Publication Date
Mon Dec 10 2018
Journal Name
Day 1 Mon, December 10, 2018
Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date
Sun Feb 25 2018
Journal Name
Advances In Bioresearch
Survey of field crickets (Orthoptera, Grylidae) in different regions of Iraq
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Publication Date
Thu Feb 01 2024
Journal Name
Iop Conference Series: Earth And Environmental Science
Survey of Insects Associated with some Species of Cucurbitaceae in Iraq
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Abstract<p>Due to the spread of insect pests that destroys the crops belonging to the Cucurbitaceae family and led to deterioration in crop productivity in Iraq due to various reasons, the most important of which is Climate fluctuation and extreme weather events have a major impact on this problem. So, this paper was proposed to identify those species pests and prevalence. Insects were collected during the period from March 1. 2022 to October 30, 2022 from the several regions of Iraq, including: Baghdad, Babylon, Basra, Karbala, Wasit, Diyala, Saladin, and Duhok Provinces. The results showed 19 important species under 17 genera with 13 families, and five orders. The most common synonyms and dist</p> ... Show More
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Publication Date
Thu Sep 15 2022
Journal Name
Gsc Biological And Pharmaceutical Sciences
Survey and revision of storage insects from several localities of Iraq
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Publication Date
Thu Aug 22 2024
Journal Name
Indian Journal Of Ecology
Survey of Insects Fauna of Al-Tar Caves in Karbala, Iraq
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Publication Date
Tue Jul 01 2014
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
SURVEY OF THE GENUS PHYTOMYZA FALLEN,1810 (DIPTERA: AGROMYZIDAE) OF IRAQ
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    The aim of this study to survey the leaf miner Phytomyza Fallen of Iraq, many leaf plants which infested by leaf miners  were collected from several regions of Iraq.  The paper showed there are  four species of this genus during the work: Phytomyza horticola Gourear,1840; Ph. atricornis Meigen, 1838; Ph. rufipes Meigen,1830; Ph. ranunculi (Schrank,1803)

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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Publication Date
Thu Nov 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Strategic orientation and its reflection on the Learning organization: Survey study of a sample of Makers decision the Iraqi Ministry of Communications
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The current research focuses on the extent to which the strategic orientation(entrepreneurial orientation, customer orientation, technology orientation, learning orientation, and investment orientation) affects the learning organization (building common vision, systemic thinking, personal dominance, mental models, team learning)The first hypothesis to test the connection relation between research variables and The second hypothesis was to test the relationship between these variables. In order to ascertain the validity of the hypotheses, the research was based on a questionnaire questionnaire prepared according to a number of In addition to building a fifth sub-variable for the strategic orientation (investment orientation) based

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Publication Date
Wed Feb 01 2023
Journal Name
Practice Periodical On Structural Design And Construction
Leading Indicators of the Health and Well-Being of the Construction Workforce: Perception of Industry Professionals
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Publication Date
Sat Jul 08 2017
Journal Name
Neural Computing And Applications
A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment
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