Exploitation of mature oil fields around the world has forced researchers to develop new ways to optimize reservoir performance from such reservoirs. To achieve that, drilling horizontal wells is an effective method. The effectiveness of this kind of wells is to increase oil withdrawal. The objective of this study is to optimize the location, design, and completion of a new horizontal well as an oil producer to improve oil recovery in a real field located in Iraq. “A” is an oil and gas condensate field located in the Northeast of Iraq. From field production history, it is realized the difficulty to control gas and water production in this kind of complex carbonate reservoir with vertical producer wells. In this study, a horizont
... Show MoreAs cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficie
... Show MoreThis paper reports a comprehensive study on the behavior of concavely curved soffit reinforced concrete (RC) beams strengthened in flexure with carbon fiber-reinforced polymer (CFRP) composites under static loading. The main objective of this paper is to explore the effect of surface concavity on the bond performance of externally bonded wet layup CFRP sheets and laminates. An experimental program consisting of flexural strengthening of 24 RC beams with concavely curved soffits was carried out. All specimens were simply supported RC beams tested under three-point bending. Of the 24 beams, 6 beams were flat soffit RC beams, and the remainder were fabricated with concavely curved soffits with a degree of curvature that is ranging from 5 mm/m
... Show MoreA linear and nonlinear theoretical and experimental aeroelastic investigation of a wing-flap-tab typical section model undergoing two-dimensional incompressible airflow is described. The linear flutter velocity (LFV) and frequency are predicted using linear analysis. Then a freeplay structural nonlinearity is considered in the tab. The structural equations of motion have been coupled with Theodorsen aerodynamic theory to produce the theoretical aeroelastic model which is analyzed by a state space method to predict the LFV and flutter frequency. Linear piecewise function has been used to introduce the tab spring stiffness in the freeplay state. The ground vibration test is used to measure the model structural dynamic characteristics. Then th
... Show MoreWith the increasing integration of computers and smartphones into our daily lives, in addition to the numerous benefits it offers over traditional paper-based methods of conducting affairs, it has become necessary to incorporate one of the most essential facilities into this integration; namely: colleges. The traditional approach for conducting affairs in colleges is mostly paper-based, which only increases time and workload and is relatively decentralized. This project provides educational and management services for the university environment, targeting the staff, the student body, and the lecturers, on two of the most used platforms: smartphones and reliable web applications by clo
Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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