{"title":"利用物联网将有杆抽油机带入课堂","authors":"C. Teodoriu, Erik Pienknagura","doi":"10.2118/191552-MS","DOIUrl":null,"url":null,"abstract":"\n Artificial lift and particularly sucker rod pumping units are the most applied technologies for oil wells. While the equipment as such requires strong mechanical background, the use of the equipment remains solely the responsibility of petroleum engineers. Teaching them the working principles and functions of the equipment could be a challenging process when using only classroom related tools such as multimedia, short pieces of equipment, etc. As a response to this shortcoming, a new laboratory has been developed at the University of Oklahoma, which includes a large-scale pumping unit that is capable to be programed to simulate any situation in real time and use the Internet of Things to gather real time data and create tailored diagnostic tools that students and laboratory staff can utilize for many applications.\n This paper focuses on the need to add a hands-on teaching experience to the classroom, and what type of data can be mined and used to accomplish specific objectives. It is required for our future petroleum engineers that they know how to apply basic industry principles and increase problem solving skills involving machinery. The proposed laboratory would be capable to deliver all standard monitored parameters of a sucker rod pumping unit to any classroom through a networked connection and allow the students to make decisions or experiment in real time with the setup. By being a large-scale setup, students can easily observe how the unit works, visualize downhole pumping operations, identify where the sensors are placed, and learn how to use raw data for any intended purpose. In other words, the entire artificial lifting process can be seen and operated from any classroom on campus remotely, and advanced analysis can be performed by the students or staff.","PeriodicalId":441169,"journal":{"name":"Day 3 Wed, September 26, 2018","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Bringing the Sucker Rod Pumping Unit into the Classroom with the Use of the Internet of Things\",\"authors\":\"C. Teodoriu, Erik Pienknagura\",\"doi\":\"10.2118/191552-MS\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Artificial lift and particularly sucker rod pumping units are the most applied technologies for oil wells. While the equipment as such requires strong mechanical background, the use of the equipment remains solely the responsibility of petroleum engineers. Teaching them the working principles and functions of the equipment could be a challenging process when using only classroom related tools such as multimedia, short pieces of equipment, etc. As a response to this shortcoming, a new laboratory has been developed at the University of Oklahoma, which includes a large-scale pumping unit that is capable to be programed to simulate any situation in real time and use the Internet of Things to gather real time data and create tailored diagnostic tools that students and laboratory staff can utilize for many applications.\\n This paper focuses on the need to add a hands-on teaching experience to the classroom, and what type of data can be mined and used to accomplish specific objectives. It is required for our future petroleum engineers that they know how to apply basic industry principles and increase problem solving skills involving machinery. The proposed laboratory would be capable to deliver all standard monitored parameters of a sucker rod pumping unit to any classroom through a networked connection and allow the students to make decisions or experiment in real time with the setup. By being a large-scale setup, students can easily observe how the unit works, visualize downhole pumping operations, identify where the sensors are placed, and learn how to use raw data for any intended purpose. 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引用次数: 5
摘要
人工举升特别是有杆抽油机是目前应用最多的油井技术。虽然这样的设备需要强大的机械背景,但设备的使用仍然完全是石油工程师的责任。如果只使用与课堂相关的工具,如多媒体、短设备等,教他们设备的工作原理和功能可能是一个具有挑战性的过程。为了弥补这一缺陷,俄克拉何马大学(University of Oklahoma)开发了一个新的实验室,其中包括一个大型抽油机,可以通过编程实时模拟任何情况,并使用物联网收集实时数据,并创建定制的诊断工具,学生和实验室工作人员可以在许多应用中使用该工具。本文的重点是在课堂上增加实践教学经验的必要性,以及可以挖掘和使用哪些类型的数据来完成特定的目标。我们未来的石油工程师需要知道如何应用基本的工业原理,并提高解决机械问题的能力。该实验室将能够通过网络连接将有杆抽油机的所有标准监测参数发送到任何教室,并允许学生根据设置进行实时决策或实验。通过大规模设置,学生可以轻松观察设备的工作原理,可视化井下泵送操作,确定传感器的放置位置,并学习如何将原始数据用于任何预期目的。换句话说,整个人工吊装过程可以从校园的任何教室远程看到和操作,并且可以由学生或工作人员进行高级分析。
Bringing the Sucker Rod Pumping Unit into the Classroom with the Use of the Internet of Things
Artificial lift and particularly sucker rod pumping units are the most applied technologies for oil wells. While the equipment as such requires strong mechanical background, the use of the equipment remains solely the responsibility of petroleum engineers. Teaching them the working principles and functions of the equipment could be a challenging process when using only classroom related tools such as multimedia, short pieces of equipment, etc. As a response to this shortcoming, a new laboratory has been developed at the University of Oklahoma, which includes a large-scale pumping unit that is capable to be programed to simulate any situation in real time and use the Internet of Things to gather real time data and create tailored diagnostic tools that students and laboratory staff can utilize for many applications.
This paper focuses on the need to add a hands-on teaching experience to the classroom, and what type of data can be mined and used to accomplish specific objectives. It is required for our future petroleum engineers that they know how to apply basic industry principles and increase problem solving skills involving machinery. The proposed laboratory would be capable to deliver all standard monitored parameters of a sucker rod pumping unit to any classroom through a networked connection and allow the students to make decisions or experiment in real time with the setup. By being a large-scale setup, students can easily observe how the unit works, visualize downhole pumping operations, identify where the sensors are placed, and learn how to use raw data for any intended purpose. In other words, the entire artificial lifting process can be seen and operated from any classroom on campus remotely, and advanced analysis can be performed by the students or staff.