A Systematic Review of Deep Learning and Quantum Reinforcement Learning for Secure Predictive Maintenance in Upstream Oil and Gas Systems
DOI:
https://doi.org/10.24191/jcrinn.v11i2.601Keywords:
PdM, Machine Learning, Deep Learning, Reinforcement Learning, Quantum Reinforcement Learning, Upstream oil and gas, Industrial AnalyticsAbstract
Predictive maintenance (PdM) plays a crucial role in maintaining reliability and operational efficiency of upstream oil and gas systems. Recent developments in artificial intelligence and specifically deep learning (DL), reinforcement learning (RL), and the new quantum reinforcement learning (QRL) have increased PdM capabilities. Nonetheless, cybersecurity vulnerabilities arise with sensor-driven model integration and can undermine system integrity. This study conducts a systematic literature review following PRISMA guidelines to examine AI-based PdM in upstream oil and gas. Major scientific databases were used to retrieve relevant studies that were published between 2015 and 2025. The rigor and relevance of the studies were evaluated using a hybrid quality appraisal framework, which is a combination of methodological and machine learning-specific criteria. The findings show that DL-based approaches, especially hybrid models such as CNN-LSTM and CNN-Transformer, demonstrate high predictive accuracy and strong validation using real-world sensor data. RL methods exhibit potential for adaptive maintenance decision-making but are largely limited to simulation-based environments. QRL remains an emerging paradigm with limited empirical validation. Additionally, while cybersecurity threats such as data spoofing and adversarial attacks are increasingly recognised, few studies integrate security mechanisms into PdM frameworks. Quality assessment indicates that high-quality studies are predominantly DL-based, whereas RL and QRL studies are generally of moderate quality. The review identifies a very important gap in the designing of integrated, secure and scalable PdM systems. Future studies must aim at integrating the real-life validation, adaptive learning models, and cybersecurity resilience to facilitate strong AI-based maintenance solutions in upstream oil and gas.
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