AI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approach

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dc.contributor.authorAlmuraia, Abdulmajeed
dc.contributor.authorHe, Feiyang
dc.contributor.authorKhan, Muhammad
dc.date.accessioned2025-06-10T10:50:53Z
dc.date.available2025-06-10T10:50:53Z
dc.date.freetoread2025-06-10
dc.date.issued2025-05-01
dc.date.pubOnline2025-05-21
dc.description.abstractNatural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation.
dc.description.journalNameProcesses
dc.identifier.citationAlmuraia A, He F, Khan M. (2025) AI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approach. Processes, Volume 13, Issue 5, May 2025, Article number 1611en_UK
dc.identifier.eissn2227-9717
dc.identifier.elementsID673320
dc.identifier.issn2227-9717
dc.identifier.issueNo5
dc.identifier.paperNo1611
dc.identifier.urihttps://doi.org/10.3390/pr13051611
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24004
dc.identifier.volumeNo13
dc.languageEnglish
dc.language.isoen
dc.publisherMDPIen_UK
dc.publisher.urihttps://www.mdpi.com/2227-9717/13/5/1611
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectNGL pumpsen_UK
dc.subjectpredictive maintenanceen_UK
dc.subjectmaintenance optimisationen_UK
dc.subjectAI-based diagnosticsen_UK
dc.subjectremaining useful life (RUL)en_UK
dc.subjectMTBFs estimationen_UK
dc.subjectfuzzy logicen_UK
dc.subjectrule-based systemen_UK
dc.subjectoil and gas industryen_UK
dc.subjectdigital tool validationen_UK
dc.subject4004 Chemical Engineeringen_UK
dc.subject40 Engineeringen_UK
dc.subjectGeneric health relevanceen_UK
dc.subject9 Industry, Innovation and Infrastructureen_UK
dc.titleAI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approachen_UK
dc.typeArticle
dc.type.subtypeJournal Article

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