https://journals.ust.edu/index.php/JST <p><strong>Journal of Science and Technology</strong> is an Open Access Peer-Reviewed Journal Published Biyearly by Faculty of Engineering and Faculty of Computing and Information Technology – University of Science &amp; Technology-Yemen. The journal welcomes articles that contribute to a wide spectrum coverage of science and technology. Originality, high quality and significance of the scientific content are essentially considered. </p> <p><strong>Online ISSN</strong>: 2410-5163 </p> <p><strong>Print ISSN</strong>: 1607-2073</p> University of Science and Technology en-US Journal of Science and Technology 1607-2073 https://journals.ust.edu/index.php/JST/article/view/2332 <p>In light of technological advancements and the evolving landscape of electronic media, Artificial Intelligence (AI) has transitioned from the realm of science fiction to a tangible tool with significant potential for addressing societal challenges, particularly within the news industry. AI stands as a promising innovation framework, capable of reshaping our interactions with technology. This study delves into the realm of journalism, exploring how artificial intelligence techniques can elevate the efficiency and precision of news production. Through a comprehensive survey, we sought the perspectives of participants, providing an avenue for their personal opinions beyond the predetermined questions. Our investigation reveals a notable application of artificial intelligence in various facets, such as scrutinizing and rectifying newspaper articles, ultimately streamlining the writing process. Despite these benefits, ethical considerations loom large, necessitating a delicate balance between leveraging AI capabilities and upholding journalistic integrity, a concern emphasized by some contributors, particularly regarding workforce implications.</p> Fatima Mohammed Ali Ben Ali Mohammed Fadhl Abdullah Copyright (c) 2024 Journal of Science and Technology 2024-11-14 2024-11-14 29 2 1 4 10.20428/jst.v29i2.2332 https://journals.ust.edu/index.php/JST/article/view/2198 <p>The significant impact of reservoir characteristics including porosity, permeability, and saturation on the well productivity calls for concerted research effort towards developing promising models for high accuracy prediction. In this work, a new model for estimating rock properties of Camal field was developed using various approaches of Artificial Intelligence (AI) such as neural networks and hybrid neuro-fuzzy techniques.</p> <p>In order to achieve the objective of this study, actual porosity, permeability and saturation data collected from laboratory analysis of the core samples obtained from eight wells in Camal field. In addition, the logs data (bulk density, gamma ray, induction) was also gathered for the same wells. Among different AI approaches were applied in this study to estimate reservoir characteristics. Hybrid method are also used such as adaptive neuro-fuzzy inference system (ANFIS) for estimation reservoir properties for Camal field. The proposed models were validated through the graphical and statistical error analysis.</p> <p>An AI models to estimate different rock properties such as porosity, permeability and saturation were presented using actual field data in this work. The obtained results showed that the hybrid neuro-fuzzy method had a great potential in predicting reservoir properties among the other AI approaches. Statistical analysis and comparative study showed that the performance of proposed hybrid neuro-fuzzy model is the best one with lower root mean square error (0.05) and higher accuracy of correlation coefficient (0.98) than those obtained with other models. It was observed that artificial intelligence modeling is reliable and accurate for prediction reservoir rock properties applied for this field.</p> <p>The modeling approach presented in this work can be used as alternatives methods for determining petrophysical properties. These methods are accurate and less expensive techniques of reservoir description.</p> Adbulrahman Kadi A. Bamaga Copyright (c) 2024 Journal of Science and Technology 2024-11-14 2024-11-14 29 2 5 13 10.20428/jst.v29i2.2198