AI-Enabled Predictive Maintenance of Wellhead and Pressure Control Equipment in Upstream Oil and Gas Operations

The oil and gas wells which are operated in the upstream sector depend on wellhead and pressure-control equipment in order to maintain pressure inside acceptable limits, to regulate the production of multiple phases, and to isolate the well should the operating limits be exceeded. Because failures may take place in the production chokes, in the master and wing valves, in the surface safety valves, in the actuators, in the seals, and in the pressure monitoring equipment, such failures can result in a slowdown of production, a loss of containment, or an emergency shutdown. This review analyses in detail how artificial intelligence is able to transform the maintenance of such assets from one that is based on scheduled inspections and corrective repairs to one that is based on the current condition and is capable of making predictions. In order to answer four closely related questions the body of literature published between 2020 and 2025 was compiled: what failure mechanisms give rise toward observable data signatures; which machine-learning, deep-learning, probabilistic, and hybrid methods are suitable for carrying out anomaly detection, diagnosis, health-index estimation, and prediction of remaining useful life; what limits the application of these methods in practice; and how the predictions should be incorporated into operational and maintenance decisions. Evidence from studies on wellhead choke modelling, subsea valve prognostics, petroleum machinery, SCADA anomaly detection, pump predictive maintenance, and digital-twin research shows that the greatest real benefits are not due to any single algorithm but result from combining reliable sensing, knowledge of the operating mode, time-aware features, uncertainty estimates, and engineering constraints. Ensemble models work well with structured and noisy field data, whereas temporal deep-learning methods are useful when high-frequency historical data and representative failure cases are available. Investigations into valves with small sample sizes have shown the advantages of using probabilistic and data-model linked prognostics, and digital twins provide a way of continuously aligning asset physics with data-based intelligence. The review proposes an asset-centred deployment architecture in which detection, diagnosis, prognosis, risk ranking, and maintenance planning form a closed loop. The research priorities are the development of standardised failure taxonomies, cross-field validation, physics-informed learning, uncertainty-aware remaining life, explainable alarms, and prospective field trials that focus on safety outcomes, the avoidance of production deferment, and improvements in maintenance efficiency instead of on accuracy alone.