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 (HB) correlation provides the most accurate correlation for calculating pressure in FH-1 and FH-3 while the Beggs and Brill original (BBO) correlation proves to be the optimal fit for wells FH-2 and Gomez mechanistic model for FH-4. These correlations show the lowest root mean square (RMS) values of 11.5, 7.56, 8.889, and 6.622 for the four wells, respectively, accompanied by the lowest error ratios of 0.00692%, 0.00033%, 0.00787%, and 0.0011%, respectively. Conversely, Beggs and Brill original (BBO) correlation yields less accurate results in predicting pressure drop for FH-1 compared with other correlations. Similarly, correlations, such as Orkiszewski for FH-2, Duns and Ros for FH-3, and ANSARI for FH-4, also display less accuracy level. Notably, the study also identifies that single-phase flow dominates within the tubing string until a depth of 6000 feet in most wells, beyond which slug flow emerges, introducing significant production challenges. As a result, the study recommends carefully selecting optimal operational conditions encompassing variables such as wellhead pressure, tubing dimensions, and other pertinent parameters. This approach is crucial to prevent the onset of slug flow regime and thus mitigate associated production challenges.
The importance of the research comes from dealing with the problem of lack of traffic awareness, which causes accidents and the occurrence of human and material losses, and the research aims to study the role of satellite channels in forming traffic awareness among the public, and a sample was chosen from Baghdad consisting of (280) individuals, male and female, and used the questionnaire tool. To obtain the data, which included several questions, the results were analyzed statistically and several results were reached, the most important of which is that there is an interest among the public in following traffic programs at a rate of one to two hours to receive information through traffic programs and to identify and apply gener
... Show MoreA genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa
The aim of this research is to solve a real problem in the Department of Economy and Investment in the Martyrs establishment, which is the selection of the optimal project through specific criteria by experts in the same department using a combined mathematical model for the two methods of analytic hierarchy process and goal programming, where a mathematical model for goal programming was built that takes into consideration the priorities of the goal criteria by the decision-maker to reach the best solution that meets all the objectives, whose importance was determined by the hierarchical analysis process. The most important result of this research is the selection of the second pro
... Show MoreEducation is the most powerful and important weapon in fighting poverty and unemployment because it provides students with new skills that appropriate the new market requirements and fair distribution of natural resources. It also transfers knowledge, skills, and ethics over generations. Education aims to create effective leaders who go under training and rehabilitation process, create a new human and instill faith, generate the creativity and innovation, and keep abreast with scientific process, updating curriculum, and finally, applying the technical education
This study aims to test whether the institutions listed on the Iraq Stock Exchange have a significant correlation between the level of conservative accounting practice with the level of market share returns during the Coronavirus pandemic period as one of the policies to confront the economic repercussions of the Coronavirus pandemic. Furthermore, the sample included institutions listed on the Iraq Stock Exchange during the 2019 and 2020 years, i.e., the period before the Coronavirus pandemic and during the Coronavirus pandemic for the purpose of comparison. The market value to book value model was used, and the study found that conservative institutions had achieved the highest level of market share prices compared to non-conservat
... Show Moreالزكاة فريضة من فرائض الإسلام وعبادة من العبادات التي اقرها الشرع الإسلامي هذه الفريضة المالية التعبدية بالإضافة الى دورها المعنوي الذي يهدف الى إرساء قواعد العدالة الاجتماعية والقضاء على مشكلة الفقر في المجتمع الاسلامي وكما ينص عليها الدين الإسلامي فان لها ابعادها ووظائفها الكثيرة التي تخدم اليات العمل الاقتصادي في مجتمع يعمل وفق نظام اقتصادي أسلامي .
ان انفاق الاموال المتحصلة من فريض
... Show MoreProblem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
... Show MoreTriticale is a hybrid of wheat and rye grown for use as animal feed. In Florida, due to its soft coat, triticale is highly vulnerable to Sitophilus oryzae L. (rice weevil) and there is interest in development of methods to detect early-instar larvae so that infestations can be targeted before they become economically damaging. The objective of this study was to develop prediction models of the infestation degree for triticale seed infested with rice weevils of different growth stages. Spectral signatures were tested as a method to detect rice weevils in triticale seed. Groups of seeds at 11 different levels (degrees) of infestation, 0–62%, were obtained by combining different ratios of infested and uninfested seeds. A spectrophotometer wa
... Show MoreThe security of information represent the available protection of information and its component and guaranty the its safety and confidentiality. The absent or lack or stop of security of information and without maximum benefit may lead to lose confidence and make it burden on the company. So we must protect the company and information from the damages which may lead to the frailer of performance and loses of the company and its workers. So the security of information considered one of the potential and controlling basis to protect individuals and companies from the damages. To insure the security and confidentiality of information there are delicate, proper and trusted ways, like FIRE WALL, PASS WORD, SYPHAR
... Show MoreMachine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims
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