Predictive Maintenance of Oil and Gas Infrastructure Using AI Models: Enhancing Operational Efficiency and Economic Output

Nigeria's oil and gas sector, the bedrock of the national economy accounting for over 87% of foreign exchange earnings, suffers chronic infrastructure deterioration resulting in annual production losses estimated at $3–5 billion. Conventional preventive and reactive maintenance paradigms have proven inadequate for the scale and operational hazards of Nigerian petroleum infrastructure. This study develops, validates, and compares five AI-based predictive maintenance (PdM) models – Long Short-Term Memory (LSTM) networks, Random Forest, Support Vector Machine (SVM), XGBoost, and Temporal Fusion Transformer (TFT) – for fault detection and Remaining Useful Life (RUL) prediction, using a dataset of 52,840 multi-sensor time-series observations collected from operational Nigerian oil and gas installations over 2019–2024. The stacked ensemble model achieved the highest classification accuracy of 98.1% (F1-Score: 0.977; AUC-ROC: 0.994), outperforming the TFT (97.3%), LSTM (96.7%), and XGBoost (95.1%) individually, and substantially exceeding the conventional preventive maintenance baseline (71.4%). Economic impact analysis reveals that AI PdM deployment reduced annual unplanned downtime by 68.2% (from 4,218 to 1,342 hours), decreased maintenance expenditure by 47.3% (from $521.4M to $274.8M), and recovered an estimated $5.64 billion in previously lost production revenue annually. Macroeconomic modelling projects a 1.5 percentage point uplift in the oil sector's contribution to Nigeria's GDP. These findings establish a compelling evidence base for the systematic adoption of AI predictive maintenance across Nigeria's petroleum infrastructure and provide actionable policy recommendations for NUPRC, NNPC Limited, and international oil company (IOC) operators.