Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It also presents the optimal mud weight window for this field, which can be used to optimise the mud weights to minimise the wellbore instability issues. The results showed that an artificial neural network is a powerful tool for determining the breakout zones using the input data. The obtaining root mean square error and the determination coefficient were respectively 0.0082 and 0.959, by which the 1D MEM gave a high match between the predicted wellbore instabilities using the Mogi-failure criterion and the predicted breakout using the ANN model. Most borehole enlargements occur due to formation shear failures because of using low mud weights during drilling. The conclusion clarify the1.35 g/cc is the optimal mud weights for drilling new wells in this field of interest with fewer drilling issues.
Several directional wells have been drilled in Majnoon oilfield at wide variation in drilling time due to different drilling parameters applied for each well. This technical paper shows the importance of proper selection of the bit, Mud type, applied weight on Bit (WOB), Revolution per minute (RPM), and flow rate based on the previous wells drilled. Utilizing the data during drilling each section for directional wells that's significantly could improve drilling efficiency presented at a high rate of penetration (ROP). Based on the extensive study of three directional wells of 35 degree inclination (MJ-51, MJ-52, and MJ-54) found that the applied drilling parameters for MJ-54 and the bit type within associated drilling parameters to drill
... Show MoreMobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kern
... Show MoreIn this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi
... Show MoreDue to the availability of technology stemming from in-depth research in this sector and the drawbacks of other identifying methods, biometrics has drawn maximum attention and established itself as the most reliable alternative for recognition in recent years. Efforts are still being made to develop a user-friendly system that is up to par with security-system requirements and yields more reliable outcomes while safeguarding assets and ensuring privacy. Human age estimation and Gender identification are both challenging endeavours. Biomarkers and methods for determining biological age and gender have been extensively researched, and each has advantages and disadvantages. Facial-image-based positioning is crucial for many application
... Show Morethe visual and aesthetic characteristics of the city depending on understanding several levels of visual and aesthetic characteristics to the city, starting with the study of the physical characteristics and morphology of the city in general, and urban style to it, and then study of visual composition of the city from the optical components that affect the composition of the image (visual composition) of the city represent by pathways, borders, regions and the specific features as classified by (Kevin Lynch), and then studying the details and the attention of all elements that would confer the beautiful appearance as necessary for the needs of society and the environment in general, for example, interest in the distribution and c
... Show MoreObjectives: The study aims to: (1) assess the prevalence of phantom vibration and ringing syndrome among
nurses, (2) determine the level of job-related stress among those nurses who are working at teaching hospitals in
Al- Nasiriyah city, and (3) identify the association between job-related stress and experience of phantom
vibration and ringing syndrome.
Methodology: : A descriptive design, cross-sectional study was used for the present study was carried out
from 4th December, 2017 to the 4th April, 2018 in order to determine the association of Phantom
Vibration and Ringing Syndrome with Job - Related Stress among nurses at Teaching Hospitals in AlNasiriyah
City , on a purposive (non-probability) sample was used in t
This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (
Choosing antimicrobials is a common dilemma when the expected rate of bacterial resistance is high. The observed resistance values in unequal groups of isolates tested for different antimicrobials can be misleading. This can affect the decision to recommend one antibiotic over the other. We analyzed recalled data with the statistical consideration of unequal sample groups. Data was collected concerning children suspected to have typhoid fever at Al Alwyia Pediatric Teaching Hospital in Baghdad, Iraq. The study period extended from September 2021 to September 2022. A novel algorithm was developed to compare the drug sensitivity among unequal numbers of Salmonella typhi (S. Typhi) isolates tested with different antibacterials.
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