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Enhancing Reservoir Modeling via the Black Oil Model for Horizontal Wells: South Rumaila Oilfield, Iraq
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Horizontal wells have revolutionized hydrocarbon production by enhancing recovery efficiency and reducing environmental impact. This paper presents an enhanced Black Oil Model simulator, written in Visual Basic, for three-dimensional two-phase (oil and water) flow through porous media. Unlike most existing tools, this simulator is customized for horizontal well modeling and calibrated using extensive historical data from the South Rumaila Oilfield, Iraq. The simulator first achieves a strong match with historical pressure data (1954–2004) using vertical wells, with an average deviation of less than 5% from observed pressures, and is then applied to forecast the performance of hypothetical horizontal wells (2008–2011). The results validate the simulator’s reliability in estimating bottom-hole pressure (e.g., ±3% accuracy for HRU1 well) and water–oil ratios (e.g., WOR reduction of 15% when increasing horizontal well length from 1000 m to 2000 m). Notably, the simulator demonstrated that doubling the horizontal well length reduced WOR by 15% while increasing bottom-hole pressure by only 2%, highlighting the efficiency of longer wells in mitigating water encroachment. This work contributes to improved reservoir management by enabling efficient well placement strategies and optimizing extraction planning, thereby promoting both economic and resource-efficient hydrocarbon recovery.

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Publication Date
Wed Jan 01 2014
Journal Name
International Journal Of Computer Applications
Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Publication Date
Thu May 05 2022
Journal Name
Journal Of Taibah University For Science
Innovative economic anthocyanin dye source for enhancing the performance of dye-sensitized solar cell
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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Multiphase Flow Behavior Prediction and Optimal Correlation Selection for Vertical Lift Performance in Faihaa Oil Field, Iraq
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In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H

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Publication Date
Mon Jan 01 2024
Journal Name
Open Engineering
Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
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Abstract<p>The current study focuses on utilizing artificial intelligence (AI) techniques to identify the optimal locations of production wells and types for achieving the production company’s primary objective, which is to increase oil production from the Sa’di carbonate reservoir of the Halfaya oil field in southeast Iraq, with the determination of the optimal scenario of various designs for production wells, which include vertical, horizontal, multi-horizontal, and fishbone lateral wells, for all reservoir production layers. Artificial neural network tool was used to identify the optimal locations for obtaining the highest production from the reservoir layers and the optimal well type. Fo</p> ... Show More
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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Feasibility Study of Applying a Pattern Waterflood Without and with Peripheral Injectors for Maximizing Oil Recovery Under Limited Water Sources Availability A Case Study of Heterogeneous Giant Carbonate Reservoir
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Publication Date
Thu Oct 01 2015
Journal Name
Engineering And Technology Journal
Genetic Based Optimization Models for Enhancing Multi- Document Text Summarization
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Publication Date
Sun Dec 01 2019
Journal Name
Al-nahrain Journal Of Science
Enhancing Sparse Adjacency Matrix for Community Detection in Large Networks
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Publication Date
Wed Jan 01 2025
Journal Name
Smart Innovation, Systems And Technologies
An Exploration of Current Trends for Enhancing Multimedia Transfer Efficiency
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This report explores emerging techniques to boost multimedia transfer effectiveness, given the escalating need for improved quality and performance in multimedia interactions. The analysis involves a thorough literature assessment and comparison of present strategies to pinpoint key tendencies and propose novel approaches. The methodology involves examining recent technological enhance ments in video coding standards, quality appraisal methods, and compression tech niques. Specific domains investigated comprise firmware component architectures, 4D indexing structures, and iterative filtering frameworks. The study in addition weighs tradeoffs between video quality, encoding intricacy, and bitrate demands. Key determinations consist of

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Publication Date
Fri Jan 01 2010
Journal Name
2010 Second International Conference On Computer Research And Development
Per-Priority Flow Control (PPFC) for Enhancing Metro Ethernet QoS
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Publication Date
Sun Feb 13 2022
Journal Name
Petroleum & Coal
Laboratory-Based Correlations to Estimate Geomechanical Properties for Carbonate Tight Reservoir.
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Rock mechanical properties are critical parameters for many development techniques related to tight reservoirs, such as hydraulic fracturing design and detecting failure criteria in wellbore instability assessment. When direct measurements of mechanical properties are not available, it is helpful to find sufficient correlations to estimate these parameters. This study summarized experimentally derived correlations for estimating the shear velocity, Young's modulus, Poisson's ratio, and compressive strength. Also, a useful correlation is introduced to convert dynamic elastic properties from log data to static elastic properties. Most of the derived equations in this paper show good fitting to measured data, while some equations show scatters

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