The Role of Pre-College STEM Education in Student Enrollment in Petroleum Engineering

2021 ◽  
Author(s):  
Nayef Alyafei ◽  
Afsha Shaikh ◽  
Mohamed Gharib ◽  
Albertus Retnanto
Keyword(s):  
High School ◽  
Developing Countries ◽  
High School Students ◽  
Stem Education ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Petroleum Engineering ◽  
School Students ◽  
Gas Industry

Abstract Final-year high school students are faced with a difficult decision when selecting their undergraduate major of choice. Often, the decision is made even more difficult by uncertainty about what different majors entail. Petroleum engineering in particular is a discipline that is generally not explored within high school classrooms and therefore students lack understanding about the roles of engineers in the oil and gas industry. To combat this uncertainty, this paper explores the potential of running pre-college project-based learning programs to increase high school students’ interest in and familiarity with pursuing various undergraduate STEM disciplines and careers. More specifically, this paper provides an insight into two case studies of novel STEM education programs, developed to enhance a group of high school students’ understanding of petroleum engineering. The programs were designed to increase students’ interest in learning about the selected petroleum engineering concepts, namely polymer flooding to enhance oil recovery and multiphase fluid flow in porous media, while simultaneously providing an understanding of the current global challenges faced by the oil and gas industry. The program also aimed to engage students in learning and applying fundamental engineering skills to relatable real-world issues. These project goals will help facilitate the desire, commonly seen in recent years, of developing countries to increase their oil and gas production. This program was applied during the Summer Engineering Academy program offered by Texas A&M University at Qatar, which provides an innovative educational space for high school students. The program was conducted with the main objective of allowing the students to understand the basic concepts of petroleum engineering via short lectures as well as laboratory experimentation. Students in Grades 9-11 spent 10 days learning about petroleum engineering applications that integrated science, engineering, and technology where they designed, built, and tested an experimental setup for understanding various processes in petroleum engineering. Students were expected to solve a common problem faced in the petroleum industry. At the end of the program, the students gained an understanding of the issues and recommended unique solutions to these problems in the form of oil-recovery based projects presented to a panel of experts. This program attempted to build bridges between the STEM education pipeline of rapidly developing countries, such as Qatar, and the new demand for talent in the oil and gas sector. The details of this novel program are presented, including the content, preparation, materials used, case studies, and the resulting learning outcomes.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

2021 ◽  
Vol 73 (01) ◽  
pp. 12-13
Author(s):  
Manas Pathak ◽  
Tonya Cosby ◽  
Robert K. Perrons
Keyword(s):  
Artificial Intelligence ◽  
Science Fiction ◽  
Oil Recovery ◽  
Oil And Gas ◽  
Autonomous Systems ◽  
Oil And Gas Industry ◽  
Human Judgment ◽  
Gas Industry ◽  
The Media ◽  
And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

scholarly journals The Impacts of Remote Learning in Secondary Education: Evidence from Brazil during the Pandemic

2021 ◽  
Author(s):  
Guilherme Lichand ◽  
Carlos Alberto Dória ◽  
Onício Leal Neto ◽  
João Cossi
Keyword(s):  
High School ◽  
Developing Countries ◽  
High School Students ◽  
Secondary Education ◽  
Test Scores ◽  
Standardized Test ◽  
Standardized Test Scores ◽  
School Students ◽  
Primary And Secondary Education ◽  
Remote Learning

Abstract Background: The transition to remote learning in the context of Covid-19 could lead to dramatic setbacks for school enrollment and learning outcomes, especially in developing countries – where a multiplicity of challenges, from limited connectivity to little support from parents, are bound to limit its effectiveness. To date, however, no study has rigorously documented the educational impacts of remote learning relative to in-person classes within primary and secondary education. Quantifying the extent of those losses, as well as the extent to which resuming in-person classes in the pandemic could at least partially offset them, is urgent, as governments worldwide struggle evaluating the trade-offs between the health and educational risks of reopening schools, with vaccination rates still dragging.Methods: Taking advantage of the fact that São Paulo featured in-person classes for the lion’s share of the first school quarter of 2020, but not thereafter, we estimate the effects of remote learning on secondary education, using a differences-in-differences strategy that contrasts variation in dropout risk and standardized test scores between the first and the last school quarters in 2020 to that in 2019, when all classes were in-person. We estimate heterogeneous effects by grade, student characteristics and school characteristics. We also estimate intention-to-treat (ITT) effects of reopening schools in the pandemic through a differences-in-differences strategy, contrasting differences between middle- and high-school students within municipalities that authorized in-person classes to partially return for the latter over the last quarter of 2020, to those within municipalities that did not.Findings: Dropout risk increased by 365% under remote learning. While risk increased with local disease activity, most of it can be attributed directly to the absence of in-person classes:we estimate that dropout risk increased by no less than 247% across the State, even at the low end of the distribution of per capita Covid-19 cases. Average standardized test scores decreased by 0.32 standard deviation, as if students had only learned 27.5% of the in-person equivalent under remote learning. Learning losses did not systematically increase with local disease activity, attesting that they are in fact the outcome of remote learning, rather than a consequence of other health or economic impacts of Covid-19. Authorizing schools to partially reopen for in-person classes increased high-school students’ test scores by 20% relative to the control group.Interpretation: Results show that the societal costs of keeping schools closed in the pandemic are very large. While the learning losses that we document are at least as large as those documented in developed countries on the aftermath of the first Covid-19 wave, the dramatic surge in dropout risk is unique to developing countries. Such massive impacts are likely to bring about long-lasting effects on employment, productivity, and poverty levels. Our findings highlight that reopening schools under safe protocols can prevent those costs from growing even larger. They also caution against recent enthusiasm for remote learning in primary and secondary education outside the context of Covid-19.Funding: Research funded by the Inter-American Development Bank (IADB) as part of a partnership between IADB and the São Paulo State Education Secretariat.

Transdisciplinary Application of Functional Materials in Petroleum Engineering

2021 ◽  
Vol 1035 ◽  
pp. 649-654
Author(s):  
Gu Fan Zhao ◽  
Rui Yao Wang
Keyword(s):  
Information Technologies ◽  
Oil And Gas ◽  
New Technologies ◽  
Research Work ◽  
Functional Materials ◽  
Oil And Gas Industry ◽  
Petroleum Engineering ◽  
Densified Wood ◽  
Gas Industry ◽  
Literature Metrology

Currently, transdisciplinary integration has become increasingly close, and has gradually become the source of innovation. At the same time, petroleum engineering technologies demand more new technologies like functional materials and electronic information technologies. In order to effectively promote technological innovation and development of the petroleum engineering, it is important to continuously monitor, analyze and evaluate the latest development of the technologies outside of the oil and gas industry. This paper combines qualitative analysis of onsite demands, application cases, technical characteristics, and quantitative analysis of literature metrology, patent evaluation, technology maturity, to evaluate the application prospects of densified wood, liquid metal and poly (thioctic acid) in the field of petroleum engineering, and specific transdisciplinary suggestions are put forward. It is recommended to carry out pre-research work for the potential application of functional materials in the petroleum engineering, and it is expected to introduce new materials for downhole tools, new antennas for downhole instruments, extend long-term effectiveness of downhole plugging, and improve drilling efficiency.

Human Factors in Domain Adaptation Within the Oil and Gas Industry

2021 ◽  
Author(s):  
Iraj Ershaghi ◽  
Milad A. Ershaghi ◽  
Fatimah Al-Ruwai
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Oil And Gas ◽  
Deep Understanding ◽  
Digital Technologies ◽  
Oil And Gas Industry ◽  
Petroleum Engineering ◽  
Gas Industry ◽  
Starting Point ◽  
Domain Expertise

Abstract A serious issue facing many oil and gas companies is the uneasiness among the traditional engineering talents to learn and adapt to the changes brought about by digital transformation. The transformation has been expected as the human being is limited in analyzing problems that are multidimensional and there are difficulties in doing analysis on a large scale. But many companies face human factor issues in preparing the traditional staff to realize the potential of adaptation of AI (Artificial Intelligence) based decision making. As decision-making in oil and gas industry is growing in complexity, acceptance of digital based solutions remains low. One reason can be the lack of adequate interpretability. The data scientist and the end-users should be able to assure that the prediction is based on correct set of assumptions and conform to accepted domain expertise knowledge. A proper set of questions to the experts can include inquiries such as where the information comes from, why certain information is pertinent, what is the relationship of components and also would several experts agree on such an assignment. Among many, one of the main concerns is the trustworthiness of applying AI technologies There are limitations of current continuing education approaches, and we suggest improvements that can help in such transformation. It takes an intersection of human judgment and the power of computer technology to make a step-change in accepting predictions by (ML) machine learning. A deep understanding of the problem, coupled with an awareness of the key data, is always the starting point. The best solution strategy in petroleum engineering adaptation of digital technologies requires effective participation of the domain experts in algorithmic-based preprocessing of data. Application of various digital solutions and technologies can then be tested to select the best solution strategies. For illustration purposes, we examine a few examples where digital technologies have significant potentials. Yet in all, domain expertise and data preprocessing are essential for quality control purposes

Carbon capture and storage in the oil and gas industry: 40 years on

2017 ◽  
Vol 57 (2) ◽  
pp. 413
Author(s):  
Christopher Consoli ◽  
Alex Zapantis ◽  
Peter Grubnic ◽  
Lawrence Irlam
Keyword(s):  
Oil Recovery ◽  
Energy Demand ◽  
Carbon Capture ◽  
Large Scale ◽  
Oil And Gas ◽  
Carbon Capture And Storage ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Wide Range ◽  
And Storage

In 1972, carbon dioxide (CO2) began to be captured from natural gas processing plants in West Texas and transported via pipeline for enhanced oil recovery (EOR) to oil fields also in Texas. This marked the beginning of carbon capture and storage (CCS) using anthropogenic CO2. Today, there are 22 such large-scale CCS facilities in operation or under construction around the world. These 22 facilities span a wide range of capture technologies and source feedstock as well as a variety of geologic formations and terrains. Seventeen of the facilities capture CO2 primarily for EOR. However, there are also several significant-scale CCS projects using dedicated geological storage options. This paper presents a collation and summary of these projects. Moving forward, if international climate targets and aspirations are to be achieved, CCS will increasingly need to be applied to all high emission industries. In addition to climate change objectives, the fundamentals of energy demand and fossil fuel supply strongly suggests that CCS deployment will need to be rapid and global. The oil and gas sector would be expected to be part of this deployment. Indeed, the oil and gas industry has led the deployment of CCS and this paper explores the future of CCS in this industry.

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