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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
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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.

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Index Terms

Computer Science
Information Sciences

Keywords

Predictive Maintenance Artificial Intelligence (AI) TimeGPT Model Time-LLM Model Electrical Submersible Pumps (ESPs) Oil and Gas Industry Operational Efficiency Machine Learning Algorithms Data Analytics in Energy Sector Maintenance Strategy Optimization

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