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Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field
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Abstract<p>Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.</p>
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Publication Date
Sun Mar 29 2020
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Using Different Methods to Predict Oil in Place in Mishrif Formation / Amara Oil Field
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Fri Jan 01 2021
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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models
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Publication Date
Mon Sep 09 2024
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Научный Форум
the functioning of artificial intelligence for the development of communication skills among foreigners learning Russian
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Publication Date
Sat Feb 15 2025
Journal Name
Iraqi Journal Of Pharmaceutical Sciences
Academic Staff Perspectives on the Impact of Artificial Intelligence on Pharmaceutical Sciences Research and Writing: A Qualitative Study.
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Artificial intelligence (AI) offers significant benefits to biomedical research and academic writing. Nevertheless, using AI-powered writing aid tools has prompted worries about excessive dependence on these tools and their possible influence on writing proficiency. The current study aimed to explore the academic staff’s perspectives on the impact of AI on academic writing. This qualitative study incorporated in-person interviews with academic faculty members. The interviews were conducted in a semi-structured manner, using a predetermined interview guide consisting of open-ended questions. The interviews were done in person with the participants from May to November 2023. The data was analyzed using thematic analysis. Ten academics aged

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Publication Date
Tue Mar 01 2022
Journal Name
Journal Of Hydrology
Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: Comparative study and insight for federated learning technology
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Publication Date
Wed Jun 05 2024
Journal Name
International Journal Of Engineering Pedagogy (ijep)
The Impact of Artificial Intelligence on Computational Thinking in Education at University
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This study aims to reveal the role of one of the artificial intelligence (AI) techniques, “ChatGPT,” in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in b

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Publication Date
Fri Oct 07 2022
Journal Name
Texas Journal Of Engineering And Technology
Estimation of Pore Pressure and In-Situ Stresses for Halfaya Oil Field: A Case Study
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Publication Date
Tue Apr 02 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

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Publication Date
Wed Jan 01 2025
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Using Artificial Intelligence to Enhance Digital Media Literacy Competencies and its Role in Shaping Media Students’ Awareness of Cybersecurity: A Survey Study
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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Synthesis and Characterization of New nano catalyst Mo-Ni /TiO2- γAl2O3 for Hydrodesulphurization of Iraqi Gas Oil
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   A new nano-sized NiMo/TiO2-γ-Al2O3 was prepared as a Hydrodesulphurization catalyst for Iraqi gas oil with sulfur content of 8980 ppm, supplied from Al-Dura Refinery. Sol-gel method was used to prepare TiO2- γ-Al2O3 nano catalyst support with 64% TiO2, 32% Al2O3, Ni-Mo/TiO-γ-Al2O3 catalyst was prepared under vacuum impregnation conditions to loading metals with percentage 3.8 wt.% and 14 wt.% for nickel and molybdenum respectively while the percentage for alumina, and titanium became 21.7, and 58.61 respectively. The synthesized TiO2- γ-Al2O3 nanocomposites and Ni-Mo /TiO2

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