Predictive Maintenance in the Oil and Gas Industry: A Literature-Based Analysis of Techniques and Their Impact on Equipment Reliability

Abaas Abdulmaged

doi:10.52716/jprs.v16i3.1117

Authors

Abaas Abdulmaged Ministry of Oil, State Company for Gas Filling and Services, Baghdad, Iraq.

DOI:

https://doi.org/10.52716/jprs.v16i3.1117

Keywords:

Accuracy, Machine Learning, Oil and Gas, Predictive Maintenance, Reliability.

Abstract

The global supply of energy depends heavily on the oil and gas industry nevertheless unexpected equipment failures cause substantial financial consequences alongside operational interruptions. Predictive Maintenance )PdM( has gained popularity because it increases reliability while reducing costs through IoT sensors and real-time data applications of machine learning. Existent scholarly research demonstrates restricted effectiveness of these methods in industrial domains because of extreme operational parameters and complicated data acquisition methods and substantial implementation expenses. The analysis employs systematized reviews of PdM techniques used in the oil and gas industry to assess their utility while identifying functional implementation challenges. The fundamental achievement of this study produced an extensive evaluation of PdM approaches which measured their measurement accuracy and cost-saving potential while demonstrating reliability improvement gains. The research team used PRISMA guidelines for conducting a systematic review which included 1,842 peer-reviewed scholarly works from 2015 to 2023 and obtained 1,325 studies after eliminating duplicates. The application of predictive maintenance by Aramco, Shell and ExxonMobil reveals that companies can achieve $2.8M annual cost savings as well as a 28% improvement in mean time between failures (MTBF). The studies demonstrate that Random Forest exhibits 85.7% accuracy while LSTM detects faults twenty-two percent earlier than traditional systems do. The results from statistical tests (including ANOVA alongside ROC curves) validate that AI-powered PdM gives better outcomes than established approaches do. The Weibull distribution demonstrates that reliability has improved because the objects show a 63.2% chance of failing after 2,000 hours

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