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.
Learning programming is among the top challenges in computer science education. A part of that, program visualization (PV) is used as a tool to overcome the high failure and drop-out rates in an introductory programming course. Nevertheless, there are rising concerns about the effectiveness of the existing PV tools following the mixed results derived from various studies. Student engagement is also considered a vital factor in building a successful PV, while it is also an important part of the learning process in general. Several techniques have been introduced to enhance PV engagement; however, student engagement with PV is still challenging. This paper employed three theories—constructivism, social constructivism and cognitive load t
... Show MoreThe research aims to show the relationship between artificial intelligence in accounting education and its role in achieving sustainable development goals in the Kingdom of Bahrain. The research dealt with the role of artificial intelligence applications in accounting education at the University of Applied Sciences as a model for Bahraini universities to achieve sustainable development goals. The application of artificial intelligence in accounting education achieves seven of the seventeen sustainable development goals. It also concludes that there is an artificial intelligence infrastructure in the Kingdom of Bahrain, as it occupies a leading regional position in digital transformation, as Bahrain ranks first in the Arab world i
... Show MoreArtificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
... Show MoreArtificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
... Show MoreIn this work, the adsorption of reactive yellow dye (Remazol yellow FG dye) by granular activated carbon (GAC) was investigated using batch and continuous process. The batch process involved determination the equilibrium isotherm curve either favorable or unfavorable by estimation relation between adsorption capacity and concentration of dye at different dosage of activated carbon. The results were fitted with equilibrium isotherm models Langmuir and Freundlich models with R2value (>0.97). Batch Kinetic study showed good fitting with pseudo second order model with R2 (0.987) at contact time 5 h. which provesthat the adsorption is chemisorptions nature. Continuous study was done by fixed bed column where breakthrough time was increased
... Show MoreThe flavonoglycone hesperidin is recognized as a potent anti-inflammatory, anticancer, and antioxidant agent. However, its poor bioavailability is a crucial bottleneck regarding its therapeutic activity. Gold nanoparticles are widely used in drug delivery because of its unique properties that differ from bulk metal. Hesperidin loaded gold nanoparticles were successfully prepared to enhance its stability and bioactive potential, as well as to minimize the problems associated with its absorption. The free radical scavenging activities of hesperidin, gold nanoparticles, and hesperidin loaded gold nanoparticles were compared with that of Vitamin C and subsequently evaluated in vitro using 2,2-diphenyl-1-picrylhydrazyl assay. The antioxi
... Show MoreThe increasing use of polymeric materials in the daily life, leads to challenges in the processing industry to deliver high performance materials with affordable terms. However, new processing techniques lead to high costs. In order to reduce processing costs it is necessary to understand the non-Newtonian behavior of the polymers in their molten state to be able to simulate the processes before the construction of the plants starts. Here the shear thinning behavior of the viscosity of polymeric melts is essential. Thus, this paper deals with the experimental investigation of the thermo-rheological behavior of the viscosity of one of the most used polymers (Polypropylene) over a wide range of temperatures and shear rates. Furthermo
... Show MoreIn Iraq most of the small buildings deployed a conventional air conditioning technology which typically uses electrically driven compressor systems which exhibits several clear disadvantages such as high energy consumption, high electricity at peak loads. In this work a thermal performance of air conditioning system combined with a solar collector is investigated theoretically. The hybrid air conditioner consists of a semi hermetic compressor, water cooled shell and tube condenser, thermal expansion valve and coil with tank evaporator. The theoretical analysis included a simulation for the solar assisted air-conditioning system using EES software to analyze the effect of different parameters on the power consumption of c
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