Big Data in Oil and Gas Industry. A New Project Base Learning Technique for Students

Author:

Tarnu Lucian Ion1,Chis Timur2,Stoianovici Doru2,Mohammad Reem Sabah2

Affiliation:

1. Lucian Blaga University, Victoriei Blv. Nr.10, Sibiu, 550024 Romania

2. Oil and Gas University, Bucuresti Blv. Nr.39, Ploiesti, 100680 Romania

Abstract

The oil-gas and energy industry (extraction, processing, and supply of fossil and renewable resources), requires the processing of thousands of process data in short times (pressure, volume, flow, position, speed, concentration). In the context of the need to increase energy security, safety in operation and especially the obligation to ensure safe control (not affected by cyber wars), the understanding and use of artificial intelligence by students (in the management of technological processes) requires the modification of learning concepts and professional training. Starting from the increased digital skills of the students, the possibility of using PBL (Project Base Learning) in understanding chemical processes and industrial processing data was created within the technological higher education of the University of Oil and Gas. The method consists of the creation together with student teams of software dedicated to artificial intelligence, for the students to understand their role in the management of technological processes and especially in the design of security in the operation of installations. The teams were formed by the leader, critic, project manager and technologist engineer and each team described a technological installation and the problems that may arise. Thus, at the end of the semester, each team presented a security plan and economic operation of the facility, as well as the software created for this purpose. This paper presents a PBL technique in Oil and Gas University and the role of this project activity in learning engineering studies.

Publisher

North Atlantic University Union (NAUN)

Reference7 articles.

1. R. K. Sawyer “The Cambridge Handbook of the Learning Sciences”. New York: Cambridge University Press, 2006, p.19-35, ISBN-13 978-0-521-60777-3.

2. A. Baaziz, L. Quoniam “How to use big data technologies to optimize operations in upstream petroleum industry”. International Journal of Innovation- IJI, 2013, 1(1), p.19-25.

3. U. Berna, T. Chis, O. Chelai, V. Sá, H. Yıldırım, D. Căprioară “PBL Applications in Teaching Topics related with Data”. Higher Education in a Digital Era through Project-based E-learning, 2023, p. 187-198.

4. W. N. Bender, “Project-Based Learning: Differentiating Instruction for the 21st Century”. Thousand Oaks, CA: Corwin Press, 2012, p. 42. ISBN: 978-1-4522-7927-5.

5. N., R. Sarrazin “Problem-Based Learning in the College Music Classroom”. Routledge. ISBN: 978-1-351-26522- 5.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3