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Optimizing Well Placement with Genetic Algorithms: A Case Study
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Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a type of stochastic optimization technique that has proven effective in solving various problems. The results of the study show significant improvements in NPV when using genetic algorithms compared to traditional methods, particularly for problems with numerous decision variables. The findings suggest that genetic algorithms are a promising tool for optimizing well placement in oil field development, improving NPV, and reducing the risk of project failure.

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Publication Date
Thu Mar 19 2015
Journal Name
Al-academy
Theatrical Visual System between the Cultural Identity and Globalization Culture Iraqi Theatre as Case Study
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Publication Date
Thu Aug 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
Relationship between supplier evaluation and family brand strategy Case study in battery factory\ Al-Waziriya
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   The research deals with the relationship between supplier evaluation (single variable) and family brand strategy (single variable) a case study in the battery factory\Al-Waziriya, and the fact that the industrial sector represents a cornerstone for building the country’s economy of and their development. The research has been selected on this basis. The problem stems from the lack of business understanding of the real role played by the assessment of the suppliers' and its strong impact on its reputation and position in the market. The research gains its importance by moving away from traditional marketing style in terms of characteristics related to the resource itself, and the service provided by the factory to c

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Publication Date
Tue Aug 18 2020
Journal Name
Journal Of Petroleum Exploration And Production Technology
Integrated reservoir characterization and quality analysis of the carbonate rock types, case study, southern Iraq
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Abstract<p>The reservoir characterization and rock typing is a significant tool in performance and prediction of the reservoirs and understanding reservoir architecture, the present work is reservoir characterization and quality Analysis of Carbonate Rock-Types, Yamama carbonate reservoir within southern Iraq has been chosen. Yamama Formation has been affected by different digenesis processes, which impacted on the reservoir quality, where high positively affected were: dissolution and fractures have been improving porosity and permeability, and destructive affected were cementation and compaction, destroyed the porosity and permeability. Depositional reservoir rock types characterization has been identified de</p> ... Show More
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Publication Date
Sat Jun 15 2024
Journal Name
Al-academy
Pure Continuity in Drama: The Case Study of the Theoretical Performance of Gilgamesh Who Saw
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تعد الديمومة الخالصة من المصطلحات الفلسفية التي اشار اليها الفيلسوف الفرنسي (هنري برغسون)، ويعني الانتقال من حالة الى اخرى بفعل الزمان والمكان والعوامل النفسية. اذ وجد الباحث ان هناك ثلاثة انواع للديمومة وهي: الديمومة الكيفية والتطويرية والامتدادية ووضع الباحث هدفاً للبحث تلخص: (الكشف عن مستويات الديمومة الخالصة في العرض المسرحي)، واتبع الباحث المنهج الوصفي في تحليل العينة القصدية لعرض مسرحية كلكامش ال

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Global Pharma Technology
Study of the genetic diversity in three species belonging to family: Daphniidae (Crustaceae, Cladocera) collected from different regions in Baghdad Province/Iraq
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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms
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The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r

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Publication Date
Mon Dec 01 2025
Journal Name
Case Studies In Construction Materials
Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms
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Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia

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Publication Date
Thu Sep 18 2025
Journal Name
Sustainable Engineering And Innovation
Using fruit fly and dragonfly optimization algorithms to estimate the Fama-MacBeth model
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This research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-f

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Publication Date
Wed Dec 01 2021
Journal Name
Civil And Environmental Engineering
Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
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Abstract<p>Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postpo</p> ... Show More
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Publication Date
Tue Jun 30 2026
Journal Name
Journal Of Economics And Administrative Sciences
Nonparametric Control Charts Estimation Using Hybrid Cyber-Intelligence Algorithms for Stock Market Monitoring
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This paper introduces a novel nonparametric hybrid cyber-intelligence-based statistical process control and anomaly detection framework in time series data. It is developed to overcome the shortcomings of the classical control schemes in dealing with complex, abnormal, and noisy input data, especially when it is autocorrelated. The proposed methodology combines three technical pillars: First, it utilizes a bidirectional long-short-term memory architecture (Bi-LSTM) to capture long-term time dependency and learn nonlinear patterns, leaving only true deviations as residuals that remove trends and noises from the market. Second, it adopts the Golden Eagle Optimizer (GEO) algorithm for optimal parameter selection. This intelligent algor

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