Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data that was adopted by the ANN study was used here where it is comprised of 1922 measured points of SSW and the other nine parameters of Gamma Ray, Compressional Sonic, Caliper, Neutron Log, Density Log, Deep Resistivity, Azimuth Angle, Inclination Angle, and True Vertical Depth from one Iraqi directional well. Three existing empirical correlations are based only on Compressional Sonic Wave Time (CSW) for predicting SSW. In the same way of developing previous correlations, a fourth empirical correlation was developed by using all measured data points of SSW and CSW. A comparison demonstrated that utilizing ANN was better for SSW predicting with a higher R2 equal to 0.966 and lower other statistical coefficients than utilizing four empirical correlations, where correlations of Carroll, Freund, Brocher, and developed fourth had R2 equal to 0.7826, 0.7636, 0.6764, and 0.8016, respectively, with other statistical parameters that show the new developed correlation best than the other three existing. The use of ANN or new developed correlation in future SSW calculations is relevant to decision makers due to a number of limitations and target SSW accuracy.
Preserving and saving energy have never been more important, thus the requirement for more effective and efficient heat exchangers has never been more important. However, in order to pave the way for the proposal of a truly efficient technique, there is a need to understand the shortcomings and strengths of various aspects of heat transfer techniques. This review aims to systematically identify these characteristics two of the most popular passive heat transfer techniques: nanofluids and helically coiled tubes. The review indicated that nanoparticles improve thermal conductivity of base fluid and that the nanoparticle size, as well as the concentrations of the nanoparticles plays a major role in the effectiveness of the nanofluids.
... Show MoreDelays occur commonly in construction projects. Assessing the impact of delay is sometimes a contentious
issue. Several delay analysis methods are available but no one method can be universally used over another in
all situations. The selection of the proper analysis method depends upon a variety of factors including
information available, time of analysis, capabilities of the methodology, and time, funds and effort allocated to the analysis. This paper presents computerized schedule analysis programmed that use daily windows analysis method as it recognized one of the most credible methods, and it is one of the few techniques much more likely to be accepted by courts than any other method. A simple case study has been implement
Ultraviolet light radiation is applied to treat Plaque Psoriasis disease by targeted phototherapy. This is available through Narrowband-UVB light radiation devices peaked at wavelength 311 nm. Ten cases were chosen as a study group, 8 males aged 22-40 years old, and 2 females aged 25 and 32 years old who were exposed to ultraviolet light radiation. Their recovery or improvement was followed weekly. Different doses were used according to the severity of the lesion and as a trial for the outcome. The dose was given two times a week, starting with 200mJ/cm2, and subsequently increased by 100 or 200 mJ/cm2 reaching a maximum dose as tolerated by each individual patient. Improvement was observed after 4 – 6 weeks. The
... Show MoreAssociation rules mining (ARM) is a fundamental and widely used data mining technique to achieve useful information about data. The traditional ARM algorithms are degrading computation efficiency by mining too many association rules which are not appropriate for a given user. Recent research in (ARM) is investigating the use of metaheuristic algorithms which are looking for only a subset of high-quality rules. In this paper, a modified discrete cuckoo search algorithm for association rules mining DCS-ARM is proposed for this purpose. The effectiveness of our algorithm is tested against a set of well-known transactional databases. Results indicate that the proposed algorithm outperforms the existing metaheuristic methods.
Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
... Show MoreFraud Includes acts involving the exercise of deception by multiple parties inside and outside companies in order to obtain economic benefits against the harm to those companies, as they are to commit fraud upon the availability of three factors which represented by the existence of opportunities, motivation, and rationalization. Fraud detecting require necessity of indications the possibility of its existence. Here, Benford’s law can play an important role in direct the light towards the possibility of the existence of financial fraud in the accounting records of the company, which provides the required effort and time for detect fraud and prevent it.
Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation. Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota
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