The week's pick
Random Articles
Reseach Article
A Comparative Analysis of TimeGPT and Time-LLM in Predicting ESP Maintenance Needs in the Oil and Gas Sector
by
Abhay Dutt Paroha,
Aakash Chotrani
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 8 |
| Year of Publication: 2024 |
| Authors: Abhay Dutt Paroha, Aakash Chotrani |
10.5120/ijca2024923426
|
Abhay Dutt Paroha, Aakash Chotrani . A Comparative Analysis of TimeGPT and Time-LLM in Predicting ESP Maintenance Needs in the Oil and Gas Sector. International Journal of Computer Applications. 186, 8 ( Feb 2024), 1-10. DOI=10.5120/ijca2024923426
@article{
10.5120/ijca2024923426,
author = {
Abhay Dutt Paroha,
Aakash Chotrani
},
title = { A Comparative Analysis of TimeGPT and Time-LLM in Predicting ESP Maintenance Needs in the Oil and Gas Sector },
journal = {
International Journal of Computer Applications
},
issue_date = { Feb 2024 },
volume = { 186 },
number = { 8 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = {
1-10
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume186/number8/a-comparative-analysis-of-timegpt-and-time-llm-in-predicting-esp-maintenance-needs-in-the-oil-and-gas-sector/
},
doi = { 10.5120/ijca2024923426 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-29T03:28:31.555871+05:30
%A Abhay Dutt Paroha
%A Aakash Chotrani
%T A Comparative Analysis of TimeGPT and Time-LLM in Predicting ESP Maintenance Needs in the Oil and Gas Sector
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 8
%P 1-10
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This research evaluates the application of advanced artificial intelligence models, TimeGPT and Time-LLM, for predictive maintenance of Electrical Submersible Pumps (ESPs) in the upstream oil and gas industry. The study meticulously analyzes the models’ proficiency in forecasting maintenance needs, aiming to augment operational efficiency and reduce unplanned downtimes. Utilizing a dataset rich in essential operational parameters, the comparative analysis reveals TimeGPT’s marginally superior performance, with an accuracy of 95.2%, precision of 92.8%, recall of 94.1%, and an AUC-ROC of 0.971. In contrast, Time-LLM achieves an accuracy of 93.6%, precision of 90.5%, recall of 91.2%, and an AUC-ROC of 0.957. Both models effectively identify critical indicators of ESP health, aligning with established industry knowledge. The integration challenges of these AI models into existing industrial setups are discussed, underscoring the necessity for high-quality data and system compatibility. The study suggests future research directions, emphasizing model refinement, economic impact assessment, and AI technology’s ethical and environmental considerations. This research provides significant insights into the use of AI in industrial maintenance, marking a stride toward more proactive and datadriven operational strategies in the oil and gas sector.
References
- Ramez Abdalla, Denis Nikolaev, David G¨onzi, Roman Manasipov, Andreas Schweiger, and Michael Stundner. Deep insight into electrical submersible pump maintenance: A predictive approach with deep learning. In SPE Offshore Europe Conference and Exhibition, page D021S006R004. SPE, 2023.
- Ramez Abdalla, Hanin Samara, Nelson Perozo, Carlos Paz Carvajal, and Philip Jaeger. Machine learning approach for predictive maintenance of the electrical submersible pumps (esps). ACS omega, 7(21):17641–17651, 2022.
- Mohannad Abdelaziz, Rafael Lastra, and JJ Xiao. Esp data analytics: Predicting failures for improved production performance. In Abu Dhabi international petroleum exhibition & conference. OnePetro, 2017.
- Shaikha AlBallam. Applying machine learning models to diagnose failures in electrical submersible pumps. 2022.
- Sahab Ali. The concept of energy efficiency technologies in the upstream petroleum industry: A literature review. Master’s thesis, Høgskolen i Molde-Vitenskapelig høgskole i logistikk, 2020.
- Serkan Ayvaz and Koray Alpay. Predictive maintenance system for production lines in manufacturing: A machine learning approach using iot data in real-time. Expert Systems with Applications, 173:114598, 2021.
- conceal. Time-llm: Time series forecasting by reprogramming larage language models. https://blog.csdn.net/ weixin_44965236/article/details/133739374, 2023. Accessed: 19-02-2024.
- Maria Drakaki, Yannis L Karnavas, Ioannis A Tziafettas, Vasilis Linardos, and Panagiotis Tzionas. Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey. Journal of Industrial Engineering and Management (JIEM), 15(1):31–57, 2022.
- Olakunle Elijah, Pang Ai Ling, Sharul Kamal Abdul Rahim, Tan Kim Geok, Agus Arsad, Evizal Abdul Kadir, Muslim Abdurrahman, Radzuan Junin, Augustine Agi, and Mohammad Yasin Abdulfatah. A survey on industry 4.0 for the oil and gas industry: Upstream sector. IEEE Access, 9:144438– 144468, 2021.
- Emmanuel I Epelle and Dimitrios I Gerogiorgis. A review of technological advances and open challenges for oil and gas drilling systems engineering. AIChE Journal, 66(4):e16842, 2020.
- Sherif Fakher, Abdelaziz Khlaifat, M Enamul Hossain, and Hashim Nameer. Rigorous review of electrical submersible pump failure mechanisms and their mitigation measures. Journal of Petroleum Exploration and Production Technology, 11:3799–3814, 2021.
- Azul Garza and Max Mergenthaler-Canseco. Timegpt-1. arXiv preprint arXiv:2310.03589, 2023.
- Supriya Gupta, Luigi Saputelli, and Michael Nikolaou. Big data analytics workflow to safeguard esp operations in realtime. In SPE Artificial Lift Conference and Exhibition- Americas?, page D021S004R003. SPE, 2016.
- Dennis Harris, Mark Banman, and David Malone. Design and qualification testing of esp cable to improve esp system run life. In SPE Gulf Coast Section Electric Submersible Pumps Symposium?, page D021S003R003. SPE, 2019.
- Nico Jansen Van Rensburg, Lisa Kamin, and Skip Davis. Using machine learning-based predictive models to enable preventative maintenance and prevent esp downtime. In Abu Dhabi International Petroleum Exhibition and Conference, page D021S043R004. SPE, 2019.
- Veronica Jaramillo Jimenez, Noureddine Bouhmala, and Anne Haugen Gausdal. Developing a predictive maintenance model for vessel machinery. Journal of Ocean Engineering and Science, 5(4):358–386, 2020.
- Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan- Fang Li, Shirui Pan, et al. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728, 2023.
- Anne Kwong, Junaid Hussain Muzamal, and Zohaib Khan. Privacy pro: Spam calls detection using voice signature analysis and behavior-based filtering. In 2022 17th International Conference on Emerging Technologies (ICET), pages 184– 189. IEEE, 2022.
- Paola Lara, Mario S´anchez, and Jorge Villalobos. Enterprise modeling and operational technologies (ot) application in the oil and gas industry. Journal of Industrial Information Integration, 19:100160, 2020.
- Patrick Luk. Design challenge of high-speed high-power density motor for advanced electrical submersible pump. In 2023 IEEE International Electric Machines & Drives Conference (IEMDC), pages 1–6. IEEE, 2023.
- Marek Moleda, Bo˙zena Małysiak-Mrozek, Weiping Ding, Vaidy Sunderam, and Dariusz Mrozek. From corrective to predictive maintenance—a review of maintenance approaches for the power industry. Sensors, 23(13):5970, 2023.
- LF Pastre and A Fastovets. The evolution of esp technology in the north sea: a reliability study based on historical data and survival analysis. In SPE Russian Petroleum Technology Conference. OnePetro, 2017.
- Marco Peixeiro. Timegpt: The first foundation model for time series forecasting. https://towardsdatascience.com/ timegpt-the-first-foundation-model-for-time-series-forecasting-bf0a75e63b3a, 2023. Accessed: 19-02-2024.
- Yongyi Ran, Xin Zhou, Pengfeng Lin, Yonggang Wen, and Ruilong Deng. A survey of predictive maintenance: Systems, purposes and approaches. arXiv preprint arXiv:1912.07383, 2019.
- Luis Ribeiro and Jose Barata. Re-thinking diagnosis for future automation systems: An analysis of current diagnostic practices and their applicability in emerging it based production paradigms. Computers in Industry, 62(7):639–659, 2011.
- Simon Robatto Simard, Michel Gamache, and Philippe Doyon-Poulin. Current practices for preventive maintenance and expectations for predictive maintenance in east-canadian mines. Mining, 3(1):26–53, 2023.
- Mehar Sahu, Rohan Gupta, Rashmi K Ambasta, and Pravir Kumar. Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis. Progress in Molecular Biology and Translational Science, 190(1):57– 100, 2022.
- Ahmed Salah, Ahmed Sabaa, A Samir Abd Elhaleem, Ahmed Medhat, Ahmed Diaa, and Osama Abu-hozifa. Esp design improvements associated with downhole slotted gravitational filter and downhole chemical treatment prolong esps run life in western desert of egypt: Case study. In Abu Dhabi International Petroleum Exhibition and Conference, page D021S051R006. SPE, 2023.
- Luigi Saputelli, Bravo Cesar, Nikolaou Michael, Lopez Carlos, Ron Cramer, Mochizuki Toshi, and Giuseppe Moricca. Best practices and lessons learned after 10 years of digital oilfield (dof) implementations. In SPE Kuwait Oil and Gas Show and Conference, pages SPE–167269. SPE, 2013.
- Luigi Saputelli, Carlos Palacios, and Cesar Bravo. Case studies involving machine learning for predictive maintenance in oil and gas production operations. In Machine Learning Applications in Subsurface Energy Resource Management, pages 313–336. CRC Press, 2022.
- Andreas Theissler, Judith P´erez-Vel´azquez, Marcel Kettelgerdes, and Gordon Elger. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215:107864, 2021.
- Rama S Velmurugan and Tarun Dhingra. Maintenance strategy selection and its impact in maintenance function: A conceptual framework. International Journal of Operations & Production Management, 35(12):1622–1661, 2015.
- Tiago Zonta, Cristiano Andr´e Da Costa, Rodrigo da Rosa Righi, Miromar Jose de Lima, Eduardo Silveira da Trindade, and Guann Pyng Li. Predictive maintenance in the industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150:106889, 2020.
Index Terms
Computer Science
Information Sciences
Keywords