Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of six oil wells were approved for the purpose of the study, where it was noted that there are many factors affecting the NPT, which differ from one well to another. Its impact was limited to drilling rig, mud pump and equipment failure. There is also a difference between the planned program and what is actually happening on the ground, due to several reasons, including human errors during the implementation of the drilling program and others due to technical errors, Misuse of equipment, in addition to human errors related to the failure to implement the drilling program.
Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications. This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision wit
... Show MoreThis paper deals with numerical approximations of a one-dimensional semilinear parabolic equation with a gradient term. Firstly, we derive the semidiscrete problem of the considered problem and discuss its convergence and blow-up properties. Secondly, we propose both Euler explicit and implicit finite differences methods with a non-fixed time-stepping procedure to estimate the numerical blow-up time of the considered problem. Finally, two numerical experiments are given to illustrate the efficiency, accuracy, and numerical order of convergence of the proposed schemes.
A specialized irradiation instrument "created instrument" was designed and created from various kinds and sizes of available plastic household-waste materials. In addition, a neutron beam collimator with a lid was designed and implemented. The collimator is with dimensions of 25 cm in height and 10 cm in inner diameter, while the lid dimensions are 11.5 cm height and outer diameter of 9.9 cm to perfectly match the inner diameter of the collimator with the possibility of movement (opening and closing), and also the shielding of the radioactive 241Am/Be neutron source with a recent activity of 37.5 mCi.
To investigate the efficiency of the "created instrument", ten hydrogenous material samples (ordinary p
... Show MoreThe aim of this research is to recognize the tasks undertaken by the headmasters of intermediate schools concerning time- administration, in accordance to the viewpoints of the headmasters of intermediate schools in the Administration of Education of Al-Karkh the Third. The sample of this research consists of (60) headmasters and &n
... Show MoreIn this paper, a numerical approximation for a time fractional one-dimensional bioheat equation (transfer paradigm) of temperature distribution in tissues is introduced. It deals with the Caputo fractional derivative with order for time fractional derivative and new mixed nonpolynomial spline for second order of space derivative. We also analyzed the convergence and stability by employing Von Neumann method for the present scheme.
This paper presents an efficient system using a deep learning algorithm that recognizes daily activities and investigates the worst falling cases to save elders during daily life. This system is a physical activity recognition system based on the Internet of Medical Things (IoMT) and uses convolutional neural networks (CNNets) that learn features and classifiers automatically. The test data include the elderly who live alone. The performance of CNNets is compared against that of state-of-the-art methods, such as activity windowing, fixed sample windowing, time-weighted windowing, mutual information windowing, dynamic windowing, fixed time windowing, sequence prediction algorithm, and conditional random fields. Th
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