The CO2-Assisted Gravity Drainage process (GAGD) has been introduced to become one of the mostinfluential process to enhance oil recovery (EOR) methods in both secondary and tertiary recovery through immiscibleand miscible mode. Its advantages came from the ability of this process to provide gravity-stable oil displacement forenhancing oil recovery. Vertical injectors for CO2 gas have been placed at the crest of the pay zone to form a gas capwhich drain the oil towards the horizontal producing oil wells located above the oil-water-contact. The advantage ofhorizontal well is to provide big drainage area and small pressure drawdown due to the long penetration. Manysimulation and physical models of CO2-AGD process have been implemented at reservoir and ambient conditions tostudy the effect of this method to improve oil recovery and to examine the most parameters that control the CO2-AGDprocess. The CO2-AGD process has been developed and tested to increase oil recovery in reservoirs with bottom waterdrive and strong water coning tendencies. In this study, a scaled prototype 3D simulation model with bottom waterdrive was used for CO2-assisted gravity drainage. The CO2-AGD process performance was studied. Also the effects ofbottom water drive on the performance of immiscible CO2 assisted gravity drainage (enhanced oil recovery and watercut) was investigated. Four different statements scenarios through CO2-AGD process were implemented. Resultsrevealed that: ultimate oil recovery factor increases considerably when implemented CO2-AGD process (from 13.5%to 84.3%). Recovery factor rises with increasing the activity of bottom water drive (from 77.5% to 84.3%). Also,GAGD process provides better reservoir pressure maintenance to keep water cut near 0% limit until gas flood frontreaches the production well if the aquifer is active, and stays near 0% limit at all prediction period for limited waterdrive.
Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d
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ABSTRACT
The research aims to know the reality of a two examined variables at the organization studied identifying the relationship between managerial processes reengineering and organizational citizenship behavior. The research applied on the Electronic Manufacturing Company encompassing a sample of managers and employees consisted of (100) individuals. A questionnaire is the main instrument for data gathering, which has been included (45) questions as well as personal interviews to support the questionnaire's questions and to achieve greater realism for collecting information.
Answers were analyzed to reach the final results through the use of a number of statistical methods via
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Abstract
Magnetic abrasive finishing (MAF) process is one of non-traditional or advanced finishing methods which is suitable for different materials and produces high quality level of surface finish where it uses magnetic force as a machining pressure. A set of experimental tests was planned according to Taguchi orthogonal array (OA) L27 (36) with three levels and six input parameters. Experimental estimation and optimization of input parameters for MAF process for stainless steel type 316 plate work piece, six input parameters including amplitude of tooth pole, and number of cycle between teeth, current, cutting speed, working gap, and finishing time, were performed by design of experiment
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In the present work, tetracycline (TC) was removed from a simulated wastewater through a new photo-anodic oxidation process with a rotating graphite cylinder anode. The effects of current density, pH, rotation speed, and NaCl addition were evaluated. The results confirmed that increasing the current density results in improving the removal of TC. However, increasing the current density beyond 5 mA/cm2 had little effect on TC removal. Results revealed that TC removal using photoanodic oxidation can be achieved at high performance with an initial pH of 5. Increasing or decreasing pH beyond this value has a negative effect on TC removal. Increasing rotation speed gave better performance for TC removal due to the increase in mass t
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The research aims to define the main and subsidiary criteria for evaluating the industrial market sectors and proposing a model for arranging these criteria according to priority and knowing the highest criteria in terms of relative importance in the General Company for Automobile Trade and Machinery, and for the purpose of establishing this model, experiences in the concerned company were approved, and this study proposes a multi-criteria decision model According to the FEAHP, the expanded fuzzy hierarchical analysis method enables the commercial company to develop clear strategic policies on which the company’s management system depends on determining criteria for evaluating and selecting market sectors and making appropriate
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Abstract
The research seeks to shed light on green accounting information systems, analyze them, identify sustainability reporting and how to improve it, as well as study the importance of the Iraqi oil sector, analyze it, and work on applying green accounting information systems in order to improve the quality of sustainability reporting. Oil as a branch of the General Corporation for the Distribution of Oil and Gas Products to apply the practical aspect and prove the hypothesis of the research. Explaining the company's role in improving environmental conditions