Layth Nabeel AlRawi
Verified @gmail.com
PhD Candidate at Department of informatics systems / Interactive system
Klagenfurt University
Layth Nabeel ALRAWI was born in Mosul, 1986. He is an operations manager for Parker Drilling Overseas in Rental & Services department in Erbil, Iraq. He has got his master’s degree in information technologies at Cankaya University, Turkey. He is now studying PhD degree in Human Computer Interaction at the Alpen Adria University Klagenfurt, Austria. His major field of study includes but not limited to human factors in information systems, user interface, and interactive design.
EDUCATION
Undergraduate degree in computer sciences from university of Mosul, Iraq in 2008.
M.Sc. degree in information technology from cankaya university, Ankara, Iraq.
Ph.D. candidate at the Alpen Adria university in Klagenfurt, Austria from 2021.
RESEARCH INTERESTS
Human Factors in information systems, user interfaces, Interactive Design, and energy informatics
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Layth Nabeel AlRawi, Lei Wang, and Troy Socha
SPE
Abstract In response to the evolving demands within the oil and gas (O&G) industry, this paper examines the integration of augmented reality (AR) headsets in remote operation support, focusing specifically on the challenges encountered, field deployment strategies, and considerations for global scalability and ongoing technology development. Four case studies—remote equipment surveys and troubleshooting, tubular thread inspection, field auditing, and rig site operations—are presented to demonstrate how these technologies can enable feasible solutions for remote support and collaboration, which transform traditional operational paradigms. By leveraging AR and related remote technologies, operational efficiency, safety, and cost-effectiveness are enhanced. However, the implementation of AR in harsh and remote environments introduces unique challenges. Key obstacles, including environmental conditions, connectivity limitations, data security, ergonomic constraints, and user adoption barriers, are highlighted and addressed through various mitigation strategies. For example, ruggedized and explosive atmospheres (ATEX)--compliant AR devices were deployed to withstand tough environmental conditions at drilling rigs. To manage connectivity issues in remote locations, private networks, and encryption protocols were implemented to ensure secure, stable communication channels necessary for real-time support. Comprehensive user training programs were introduced to facilitate smooth adoption and to deliver practical benefits to users. In addition to addressing these challenges, the impact of global deployment strategies and continuous technology development on maximizing the effectiveness of AR in the O&G industry is outlined. Significant operational improvements are indicated by the findings from these case studies, including a reduction in travel-related expenses and carbon emissions, enhanced accuracy in remote inspections, improved safety protocols, and decreased maintenance costs. These results underscore the transformative potential of AR when thoughtfully implemented with robust support systems, highlighting its ability to drive productivity and sustainability in O&G operations globally. Our findings suggest that AR, paired with comprehensive challenge mitigation strategies, offers a valuable model for future applications across the industry. With continued advancements in wearable technology, remote support can further evolve to meet the industry’s demands for operational efficiency, safety, and environmental sustainability.
Layth Nabeel AlRawi, Abdullah Hashim AlBella, and Osama Ibraheem Ashour
AIP Publishing
Layth Nabeel Alrawi, Osama Ibraheem Ashour, and Abdulrahman Zeain
IEEE
Usability is the key to develop and improve any system as it represents the direct contact point between users and machines. The use of the critical control system in the oil and gas industry is increasing. Due to the complexity of these systems, its interface usability should be assessed and developed periodically. In this research, the attributes that affect interface usability are identified. The usability of the Torque Turns System (TTS) is evaluated since the periods of downtime is projected to increase in the field. There are some works similar to our work however none of them had collected data directly from real operators from the field. An evaluation of the torque turn system interface usability is performed using questionnaire related to common interface usability attributes including accessibility, learnability, effectiveness, memorability, efficiency, safety, cognitive load, understandability, and satisfaction. The findings indicate a potential weakness in terms of understandability and accessibility
Layth Nabeel AlRawi
IEEE
Web applications have been spreading everywhere. However, this may have a number of drawbacks mostly related to usability issues. Therefore, the investigation of usability problems continues to improve these applications. In this study, usability metrics are used for web application interface evaluation. The relation between web application usability and end user performance is investigated. Many studies have evaluated interface usability in a number of sectors; however, studies on oil and gas web applications have been limited. In this study, two sessions with twelve participants were designed to obtain data directly from end users. The sessions were observation and user feedback sessions. The findings indicate a potential relation between system usability and end user performance in terms of effectiveness and satisfaction.
Layth Nabeel AlRawi and Osama Ibraheem Ashour Ashour
ACM
Sentiment analysis is the process of computationally identifying and categorizing opinions from a piece of text to determine whether the writer's attitude towards a practical topic, products or services is positive, negative or neutral. In this study, Machine Learning techniques are used to perform sentiment analysis on Oil and Gas customer feedback data. We present a comparison of different classification algorithms used for opinion mining, including Support Vector Machine (SVM), Naïve Bayes (NB), Instance Based Learning (IB3), Random Forest (RF), Partial Decision trees (PART), and Logit Boost (LB). Many studies have been performed on sentiment analysis in different sectors, but research into Oil and Gas customer feedback has been limited. Therefore, we have targeted a pathless sector, namely the Petroleum sector, where companies express their opinions towards specific products or services. Waikato Environment for Knowledge Analysis (WEKA) is used for experimental results. The WEKA environment is open source software entailing a collection of machine learning algorithms to solve data mining problems. The main aim of this study is to evaluate the efficiency of the above mentioned classifiers in terms of Precision, Recall, F-Measure and Accuracy. The findings of the comparison analysis indicate that the Naïve-Bayes classifier gives the best Accuracy of all classifiers. A small dataset could be considered as a limitation to our study due to the difficulty of gaining more datasets at the time of the research. However, this research will play a vital role for researchers in making decisions about the algorithm that they are going to use to solve their data mining problems.
Layth Nabeel Alrawi and Tolga Pusatli
ACM
System availability and efficiency are critical in the petroleum sector as any fault affecting those systems may negatively impact operations resources, such as money, human resources and time. Therefore, it has become important to investigate the reasons for such errors. In this study, human error has been targeted since a number of these errors is projected to increase in the sector. The factors that affect end user behavior are investigated in addition to an evaluation of the relation between system availability and human behavior. An investigation has been performed following the descriptive methodology in order to gain insights into human error factors. Questionnaires related to software/hardware errors and errors due to the end user were collected from 81 site workers. The findings indicate a potential relation between end user behavior and system availability. Training, experience, education, work shifts, system interface, usage of memory sticks and I/O devices were identified as factors affecting end user behavior, hence system availability and efficiency.