Developing a virtual engineering lab using ADDIE model. [Article]

In recent years, digital competence has become essential at the workplace. There is a growing demand for engineers with both employability and digital skills. As a result of the technological advancements, the Virtual Laboratory (VLab) concept was created to provide students with a safe environment to acquire the skills and enthusiasm to enhance the delivery of STEM (science, technology, engineering and mathematics) subjects. This study presents a VLab developed using ADDIE model design criteria that allows users to perform VLab in engineering education. Users were able to carry out experiments individually or collectively, creating an effective, flexible learning environment for students. This was integrated smoothly into the Campus Moodle platform learning environment. The VLab design and implementation details are explained and validated through Alpha and Beta acceptance testing. A mixed-methods research design was used to collect and analyse the data. Questionnaires were used to collect quantitative data from 144 students, and 17 of them were interviewed to collect qualitative data. The findings revealed that the VLab is a collaborative, effective and interactive learning environment, which develops graduates' knowledge, soft skills and digital skills, while also improving their competencies relevant to the enhancement of digital skills at their workplace. As a result, lecturers are recommended to use VLab to enhance the quality of teaching and advance the learning experiences of their students

Developing collaborative online project-based learning model to enhance learning in engineering.

It has been widely reported that knowledge and skills gained in formal Higher Education Institute courses are different from those required at work. Students lack soft skills, according to feedback from employers. Many universities have become proactive in improving their curricula to address industry demands. Collaborative learning (CL) is the current trend towards active learning, bridging both academia and industrial expectations. This paper presents a collaborative online project-based learning (COPBL) approach using the ADDIE (analysis, design, development, implementation, evaluation) model in order to improve the quality of learning, teaching and practice collaboration. The COPBL framework is designed to help students conduct projects independently or collaboratively in a safe and engaging manner. A set of data was obtained and analysed using a mixed-methods research design. Questionnaires were used to collect quantitative data from 155 students, and interviews were conducted with 25 of them to collect qualitative data. Analysis revealed that 90% of students engaged in group discussion to reach a decision, and students collaborated and shared information about the project with their peers. Students engaged in teamwork within their learning community and participated in group problem solving as they worked through the COPBL. They were active and motivated in group meetings and used workbooks to plan and record activities. As a result, they were able to better understand the ideas, objectives, or resources involved in the projects, as well as improve their ability to listen to and respect the ideas of others. Students believed that when they communicated and participated in group activities, they learned more. Students agreed that CL was more effective and better than individual work, that they understood the subject better, used technology better, and learned things of significant value. The findings indicated that COPBL gives students the opportunity to apply their knowledge of the discipline in the given activities, learn more advanced information from practical performance, and develop digital skills and a set of soft skills (including communication, collaboration, critical thinking, problem-solving, time management, and creative skills) that complement hard skills in the evolving job market

Genetic programming application in predicting fluid loss severity.

Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is utilised to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are utilised. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations

Models of work-based learning with examples of practice in petroleum engineering: a university-employer partnership.

Work-based learning (WBL) is a collaborative tool employed by educational institutions and industry to educate and develop their students or workforce in all three strands of learning: for, at, and through work. This paper presents petroleum engineering WBL models designed using Merrill's First Principles of Instruction for university-employer partnership programmes to meet the specific needs of employers and individual students. These models integrated activities and assessment for learning and provided experiences where WBL is used as the principal means for bringing about change in the workplace and student competency as future engineers. The models are a learning approach that exposes students to a wider industry partner to build up client-facing experience throughout their degree. This is aligned with key employability skills, which are highly valued in the job market. The results indicated that the WBL approach requires active involvement of students, educational institutions, and employers in course design, and that the WBL approach has increased professionalism and motivation among students, as well as significantly increasing graduate employability by addressing persistent skills gaps and meeting business needs. In conclusion, the WBL models demonstrated positive implications, widening access to higher education and enabling employers to shape their workforce in line with business demands while offering a high-value, low-cost option to upskill staff

New HTHP fluid loss control agent for oil-based drilling fluid from pharmaceutical waste.

In oil-based drilling fluid systems (OBM, for Oil-Based Mud) the oil phase is the major source of contamination and hydrocarbon pollution is, by far, the main concern. However, the release and accumulation of mud additives in the environment may be deleterious on grounds of volume and/or concentration. This paper describes a waste valorisation study from an antibiotic large-scale manufacturing for the preparation of a new drilling fluid additive (Fluid-Loss-Control agent: FLC) for OBM. This additive is environment-friendly, biomaterial in nature, and totally biodegradable and thus expected to have no detrimental effects on the surrounding environment and ecosystems. After product washing, heating, grinding, and screening, a cationic surfactant was used to improve fluid stability and filtration properties. Our results show that: According to drilling fluid requirements, the treatment of this pharmaceutical reject yields a product with good properties, i.e. satisfactory as far as thermal stability, filtration characteristics, and rheology property are concerned. The pre-treatment of those products (according to their original state and particle size, as well as the type and concentration of added surfactant) enhances additive quality and shows good compatibility in drilling fluid systems. The substitution of this new product for conventional additives affords good drilling fluid stability and performance under high temperature and high pressure (HTHP) conditions, economic, and environmental solutions to improve the environmental standing of OBMs further

An artificial lift selection approach using machine learning: a case study in Sudan.

This article presents a machine learning (ML) application to examine artificial lift (AL) selection, using only field production datasets from a Sudanese oil field. Five ML algorithms were used to develop a selection model, and the results demonstrated the ML capabilities in the optimum selection, with accuracy reaching 93%. Moreover, the predicted AL has a better production performance than the actual ones in the field. The research shows the significant production parameters to consider in AL type and size selection. The top six critical factors affecting AL selection are gas, cumulatively produced fluid, wellhead pressure, GOR, produced water, and the implemented EOR. This article contributes significantly to the literature and proposes a new and efficient approach to selecting the optimum AL to maximize oil production and profitability, reducing the analysis time and production losses associated with inconsistency in selection and frequent AL replacement. This study offers a universal model that can be applied to any oil field with different parameters and lifting methods

Developing a virtual engineering lab using ADDIE model.

This poster describes a project in RGU's School of Engineering that aims to develop a complete learning management product that will aim to address the limitations of hands-on laboratories and meet students' growing expectation for seamless integration of technology into their learning experience

University-employer partnerships: petroleum engineering work-based learning models using adopted Merrill's first principles of instruction.

This poster describes models for work-based learning used by the petroleum engineering industry, in partnership with the RGU School of Engineering. The reasoning and impact of such models are evaluated, and reflections made on their benefits

Developing a technology to design a collaborative online project-based learning model.

This poster describes a project that aimed to develop technology that would support the design of collaborative online project-based learning within the RGU School of Engineering. Project-based learning is reported to help students develop soft skills, which many employers believe are currently lacking among graduates

Drilling fluids filtration and impact on formation damage

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