This paper deals with studying the effect of hole inclination angle on computing slip velocity and consequently its effect on lifting capacity. The study concentrates on selected vertical wells in Rumaila field, Southern Iraq. Different methods were used to calculate lifting capacity. Lifting capacity is the most important factor for successful drilling and which reflex on preventing hole problems and reduces drilling costs. Many factors affect computing lifting capacity, so hence the effect of hole inclination angle on lifting capacity will be shown in this study. A statistical approach was used to study the lifting capacity values which deal with the effect of hole inclination angle and those values that do not put the effect of hole inclination angle under consideration. Results illustrated that low hole inclination angles had a slight effect on lifting capacity values , but this study could be used on high inclination angles like directional wells or horizontal wells , hence high hole inclinations angle will yields high effect on lifting capacity values.
In this work we investigate and calculate theoretically the variation in a number of optoelectronic properties of AlGaAs/GaAs quantum wire laser, with emphasis on the effect of wire radius on the confinement factor, density of states and gain factor have been calculated. It is found that there exist a critical wire radius (rc) under which the confinement of carriers are very weak. Whereas, above rc the confinement factor and hence the gain increase with increasing the wire radius.
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The paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie
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PPSU hollow fiber nanofiltration membranes are prepared by applying two concentrations and various extrusion pressures according to the phase inversion method. Cross-sectional area and outer structures were characterized by using scanning electron microscope (SEM) and atomic force microscopy (AFM). In additional to the pore size distribution, either the mean roughness or the mean pore size of the PPSU hollow fiber surfaces was evaluated by AFM. It was found that the morphology of the PPSU fibers had both sponge-like and finger-like structures through different extrusion pressures and PPSU concentrations. The mean pore size and mean roughness for inner and outer surfaces were seen to be decreased with the increase of extrusion pressure at
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Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa
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