Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI
Gordon Buchan A * and Muhammad Abdullah AA
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. |
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.
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. |
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
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.
| Google Scholar |
