Research on the application of artificial intelligence tools to diagnose common failure of centrifugal pumps applied to gas condensate transportation system at Hai Thach - Moc Tinh field
- Authors: Thinh Van Nguyen 1*, Truong Hung Trieu 1, Hai Thanh Tran 2, Hai Huu Ngo 3, Trung Ngoc Tran 3
1 Hanoi University of Mining and Geology, Hanoi, Vietnam
2 Russia - Vietnam Joint Venture - Viesovpetro, Ba Ria-Vung Tau, Vietnam
3 Biendong Petroleum Operating Company, Ho Chi Minh City, Vietnam
- Received: 23rd-Feb-2022
- Revised: 29th-June-2022
- Accepted: 1st-Aug-2022
- Online: 31st-Aug-2022
- Section: Oil and Gas
Mechanical equipments such as pumps, air compressors, etc. play an important role in the production, processing and transporting oil and gas since every single equipment, serves different functions. For oil and gas transportation system, pump is an essential mechanical device used to pump and transport the product. In order to promote technical features as well as high working efficiency, it is important to ensure that these devices always work the most efficiently in the best technical conditions. The application of modern scientific and technical advances to the maintenance and operation of mechanical equipment in general and pumps in particular will help to reduce the risks and bring economic benefits to the operators. This paper, therefore, presents results of the research on the application of artificial intelligence (AI) in diagnosing common failure of Condensate pums at Hai Thach - Moc Tinh field basing on analysis of field data which help to improve the efficiency of gas condensate transportation. The results helped to predict and warn early the possible failures to the Condensate pums at Hai Thach - Moc Tinh field. Results of the research can be applied to other equipment devices working in the same conditions during oil and gas production process in Vietnam.
Agwu, O.E., Akpabio, J.U., Alabi, S.B., and Dosunmu, A., (2018). Artificial intelligence techniques and their applications in drilling fluid engineering: A review. Journal of Petroleum Science and Engineering, 167, 300-315. https://doi.org/ 10.1016/j.petrol. 2018.04.019
Bello, O., Holzmann, J., Yaqoob, T., Teodoriu, C., (2015). Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art. Journal of Artificial Intelligence and Soft Computing Research.
Clydeunion, (2011). Installation Operation and Maintenance Manual (IOM) condensate export pumps CUP - BB3. Bien Dong 1 Project.
Chui, M., (2017). Artificial intelligence the next digital frontier. McKinsey and Company Global Institute, 47(3.6).
Gouriveau, R., Medjaher, K., Zerhouni, N., (2016). From Prognostics and Health Systems Management to Predictive Maintenance 1: Monitoring and Prognostics, Wiley.
Herve, P., Haddad, G., Moore, K., Rosner, M., (2018). Automated Model Building: The Next AI Frontier in Predictive Maintenance. Paper presented at the Offshore Technology Conference, Houston, Texas, USA. https://doi. org/10.4043/28634-MS.
Hoang, K.S., Trinh, X.V., Tran, V.T., Dang, A.T., (2017). Comprehensive Sanding Study from Laboratory Experiments, Modeling, Field Implementation, to Real-Time Monitoring, a Case Study for Hai Thach and Moc Tinh Fields, Offshore Viet Nam. SPE 186378 - MS, 1-9. https://doi.org/10.2118/186387-MS
Trieu, H.T., Nguyen, M.T., (2006). Study on the cooperation of centrifugal pumps in oil and gas transportation network. Proceeding of the 17th Scientific Conference, Hanoi University of Mining and Geology (in Vietnamese with English abstract).
Trieu, H.T., (2021). Researching and building a set of artificial intelligence tools to support analytical assessment, linking geological, geophysical wells and exploitation data to improve the efficiency of management and exploitation of condensate gas fields Hai Thach-Moc Tinh 05-2 and 05-3 blocks, in the East Sea of Vietnam. State-level project under "National key science and technology program for innovation and modernization of mining and mineral processing technology up to 2025", code 077.2021.CNKK.QG/HDKHCN, Ministry of Science and Technology. (in Vietnamese)