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Australian Energy Producers Journal Australian Energy Producers Journal Society
Journal of Australian Energy Producers
RESEARCH ARTICLE (Non peer reviewed)

Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI

Gordon Buchan A * and Muhammad Abdullah A
+ Author Affiliations
- Author Affiliations

A John Wood Group Plc, Aberdeen, Scotland, UK.




Gordon Buchan, an operations Director, and global lead for Wood’s maintAI program, boasts over 20 years of industry expertise. Throughout his career, Gordon has overseen diverse contracts for Wood’s global operations, spearheading a Maintenance & Reliability improvement initiative that yielded over $28 million in savings. Having collaborated with major global energy companies, he leverages this wealth of experience in every engagement. Holding a degree in mechanical engineering, Gordon consistently directs his efforts towards enhancing safety, boosting production, and achieving efficiency gains. In his role as Operations Director and maintAI Global Leader, he is dedicated to delivering data-driven, agile value creation through the implementation of transformative solutions, including maintAI, integrityAI, safetyAI, and decarbAI. Gordon’s commitment lies in driving innovation and sustainable success for the energy industry.



Muhammad Abdullah is a Data Engineer in Wood’s Digital Consulting team, focussed on digitally optimising operations and maintenance. He holds a master’s degree in petroleum data management from the University of Aberdeen and a BSc in Petroleum and Gas Engineering. Muhammad brings experience across the oil and gas industry, including reservoir engineering optimising rig performance and drilling operations. At Wood, he works on AI and machine learning solutions to extract value from client data and enhance asset productivity and safety. Muhammad is skilled in data engineering, automation, developing analytics, leveraging mathematical and economic modelling. Muhammad has authored and presented technical papers at industry conferences.

* Correspondence to: gordon.buchan@woodplc.com

Australian Energy Producers Journal 64 S105-S108 https://doi.org/10.1071/EP23277
Accepted: 5 April 2024  Published: 16 May 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of Australian Energy Producers.

Abstract

Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.

Keywords: artificial intelligence, backlog reduction, energy sector, Generative AI, GPT, LLM (large language model), maintenance optimisation, operational efficiency, predictive analytics, productivity, reliability.

Biographies

EP23277_B1.gif

Gordon Buchan, an operations Director, and global lead for Wood’s maintAI program, boasts over 20 years of industry expertise. Throughout his career, Gordon has overseen diverse contracts for Wood’s global operations, spearheading a Maintenance & Reliability improvement initiative that yielded over $28 million in savings. Having collaborated with major global energy companies, he leverages this wealth of experience in every engagement. Holding a degree in mechanical engineering, Gordon consistently directs his efforts towards enhancing safety, boosting production, and achieving efficiency gains. In his role as Operations Director and maintAI Global Leader, he is dedicated to delivering data-driven, agile value creation through the implementation of transformative solutions, including maintAI, integrityAI, safetyAI, and decarbAI. Gordon’s commitment lies in driving innovation and sustainable success for the energy industry.

EP23277_B2.gif

Muhammad Abdullah is a Data Engineer in Wood’s Digital Consulting team, focussed on digitally optimising operations and maintenance. He holds a master’s degree in petroleum data management from the University of Aberdeen and a BSc in Petroleum and Gas Engineering. Muhammad brings experience across the oil and gas industry, including reservoir engineering optimising rig performance and drilling operations. At Wood, he works on AI and machine learning solutions to extract value from client data and enhance asset productivity and safety. Muhammad is skilled in data engineering, automation, developing analytics, leveraging mathematical and economic modelling. Muhammad has authored and presented technical papers at industry conferences.

References

Brown TB, Mann B, Ryder N, et al. (2020) Language Models are Few-Shot Learners. In ‘Advances in Neural Information Processing Systems. Vol. 33’. pp. 1877–1901. Available at https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

Comerford N (2005) Crow/AMSAA Reliability Growth Plots. In ‘Proceedings of the 16th Annual Conference of Vibration Association of New Zealand’. Available at https://www.plant-maintenance.com/articles/Crow-AMSAA.pdf

Patel H, Singh A, Meier J (2019) Advancements in Reliability Analysis: Beyond Traditional Models. In ‘Proceedings of the International Conference on Industrial Engineering and Operations Management’. pp. 1123–1135. (IEOM Society)

Zhang L, Zhou Y, Li X (2021) Enhancing Equipment Maintenance Strategies with GPT-3 Powered Algorithms. International Journal of Production Research 59(7), 2142-2159.
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