Mechanical Integration in Oil and Gas Systems

Pipeline Systems, Drilling Equipment, Processing Facilities, Predictive Maintenance, Preventive Maintenance, Condition-Based Maintenance, Corrosion Control Techniques in Offshore Platforms, Steel Pipelines, Chemical Inhibitors and Enhanced Efficiency in its Operations, was synthesized to identify solutions to the problem of aging infrastructure, the challenge of corrosion and maintenance complexities. The purpose of the review was to talk about maintenance methods, compare them to best practices in corrosion prevention, find machine learning and IoT use cases, compare methods of maintenance, and discuss improvement in the efficiency of operations. The review synthesized existing worldwide research is using machine learning, probabilistic and digital twins and risk-based models. It is found that the predictive and condition-based maintenance convincingly lowers down time and the rate of failures with the aid of the up-to-date machine learning models of high precision harnessing real-time sensor data. Corrosion prevention measures that include the use of chemical protection, cathodic protection, coupled with predictive analytics are practical measures in extending the life of assets especially offshore. The combination with IoT and AI facilitates the in-time monitoring and anomaly detection which further improves the maintenance optimization despite heterogeneity in the data and cybersecurity. Cost and risk are balanced in maintenance optimization models using Markov and Bayesian techniques, but the scalability and complexity can be a weakness. All these results point out to the fact that high level mechanical systems integration increases reliability and operations productivity. The review highlights the necessity of the standardized data frameworks, interdisciplinary approach, and scalability that would allow massive adoption and optimisation of asset management in the oil and gas infrastructure.