The importance of kick tolerance in well operations has recently increased due to its implications in well design, in drilling and well control. To study a simple method for the application of kick tolerance concept in an effective way on the basis of field data, this research purpose is to improve knowledge about Kick Tolerance and represents a technical basis for the discussion on revision of standard procedure. The objective of this work is to review and to present a methodology of determination the kick tolerance parameters using the circulation kicks tolerance concepts. The proposed method allows to know, to evaluate and to analyze the kick tolerance problem in order to make the drilling execution safer and more economical by reducing the probability to have an incident. The calculations of presented methodologies were based upon calculated input values such as ppore and pfrac. and not upon measured leak-off test and RFT (less accurate) input values as in traditional methods. The paper also analyses the calculations not with KT parameters only, but it has continued to give the killing operation procedure to such high pressure high temperature (HPHT) wells.
In oil and gas well cementing, a strong cement sheath is wanted to insure long-term safety of the wells. Successful completion of cementing job has become more complex, as drilling is being done in highly deviated and high pressure-high temperature wells. Use of nano materials in enhanced oil recovery, drilling fluid, oil well cementing and other applications is being investigated. This study is an attempt to investigate the effect of nano materials on oil well cement properties. Two types of nano materials were investigated, which are Nano silica (>40 nm) and Nano Alumina (80 nm) and high sulfate-resistant glass G cement is used. The investigated properties of oil well cement included compressive strength, thickening
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In this article, a new deterministic primality test for Mersenne primes is presented. It also includes a comparative study between well-known primality tests in order to identify the best test. Moreover, new modifications are suggested in order to eliminate pseudoprimes. The study covers random primes such as Mersenne primes and Proth primes. Finally, these tests are arranged from the best to the worst according to strength, speed, and effectiveness based on the results obtained through programs prepared and operated by Mathematica, and the results are presented through tables and graphs.
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep
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Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
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Abstract: The aim of the current research is to identify (the relationship between deep understanding skills and mathematical modeling among fifth grade students) the research sample consisted of (411) male and female students of the fifth grade of biology distributed over the General Directorates of Education in Baghdad / Al-Rusafa / 2 / and Al-Karkh / 1 /, and two research tools were built: 1- A test of deep understanding skills, consisting of (20) test items and a scale for two skills. 2- The second test consists of (24) test items distributed among (18) essay items and (6) objective items. The psychometric properties of validity, stability, discriminatory strength, and effectiveness of alternatives were verified for the two tests fo
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