Wellbore instability is one of the most common issues encountered during drilling operations. This problem becomes enormous when drilling deep wells that are passing through many different formations. The purpose of this study is to evaluate wellbore failure criteria by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict a safe mud-weight window for deep wells. An integrated log measurement has been used to compute MEM components for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results. The prediction of mud weight along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations ranges between 12.5 to 15 ppg. The predicted safe mud weight value seems to be narrow with a well deviation higher than 350. Therefore, for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations, the wellbore appears unstable compared to other formations. The results of stability analyses indicate that the breakout mud weight wasn’t affected by wellbore azimuth because of low-stress contrast. Furthermore, shear failure can be prevented by drilling the well with an inclination of less than 350. As well as, to prevent breakdown the well should be drilled with an inclination between 25o to 65o in the direction of minimum horizontal stress. These outcomes could be used to prevent wellbore instability and determine a safe mud-weight window when planning to drill nearby wells in the future.
After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
... Show MoreGender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
... Show MoreEstimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes
... Show MoreThe proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue
... Show MoreThe optimum design is characterized by structural concrete components that can sustain loads well beyond the yielding stage. This is often accomplished by a fulfilled ductility index, which is greatly influenced by the arrangement of the shear reinforcement. The current study investigates the impact of the shear reinforcement arrangement on the structural response of the deep beams using a variety of parameters, including the type of shear reinforcement, the number of lacing bars, and the lacing arrangement pattern. It was found that lacing reinforcement, as opposed to vertical stirrups, enhanced the overall structural response of deep beams, as evidenced by test results showing increases in ultimate loads, yielding, and cracking of
... Show MoreGrowth is a multifactorial process influenced by genetic, nutritional, hormonal, psychosocial and other factors including the general health of a child. Epilepsy defined as a chronic condition characterized by recurrent clinical events or epileptic seizures, which occur in the absence of a metabolic or toxic disease the drugs that use in the treatment of this condition can affect patients growth due to their mechanisms of action. This study aimed to evaluate the effect of some antiepileptic drugs on growth (height and weight) in children with epilepsy. This work involved 51 newly diagnosed children with a different form of epilepsy (Generalized, absent and partial). Patients divided into three groups according to the treatment (group one
... Show MoreBackground: obesity is a major global health problem with more than 200 million obese men and almost 300 million obese women. Melatonin is a well-known molecule for its involvement in circadian rhythm regulation and has multiple pathological actions including control of appetite, sleep wake cycle and metabolic syndrome.
Aim: to estimate the effect of melatonin supplements on obese patients on a calorie restricted diet in comparison to patients on lifestyle measures only in the form of weight loss, waist circumference and sleep quality.
Subjects and Method: one hundred patients with body mass index > 24 were collected, fifty patients were starte
... Show MoreThe main objective of this study is to understand the work of the pile caps made of lightweight aerated foam concrete and study the many factors affecting the ability and the capacity of the shear. The study was done by analyzing previous practical and theoretical experiences on the reinforced concrete pile caps. The previous practical results indicated that all specimens failed by shear diagonal compression or tension modes except one specimen that failed flexural-shear mode. Based on test specimens' practical results and behavior, some theoretical methods for estimating the ultimate strength of reinforced concrete pile caps have been recommended, some of which evolved into the design documents available on the subject.
... Show MoreThis research is addressing the effect of different ferrocene concentration (0.00, 2.15x10-3, 4.30x10-3, 8.60x10-3, and 12.9x10-3) on the bulk free radical polymerization of methyl methacrylate monomer in benzene using benzoyl peroxide as initiator. The polymerization was conducted at 60º C under free oxygen atmosphere. The resulting polymers were characterized by FTIR. The results were compared with the presence and absence of ferrocene at 10% conversion. The %conversion was 3.04% with no ferrocene present in the polymerization medium and its increase to 9.06 with a first lowest ferrocene concentration added, i.e. 2.15 x10-3mol/l. This was positively reflected on the poly(methyl methacrylate) molecular weight measured by viscosity techniq
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In this research we study the wavelet characteristics for the important time series known as Sunspot, on the aim of verifying the periodogram that other researchers had reached by the spectral transform, and noticing the variation in the period length on one side and the shifting on another.
A continuous wavelet analysis is done for this series and the periodogram in it is marked primarily. for more accuracy, the series is partitioned to its the approximate and the details components to five levels, filtering these components by using fixed threshold on one time and independent threshold on another, finding the noise series which represents the difference between
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