AI-Driven Maintenance in the Oil and Gas Industry: A Paradigm Shift with Societal Implications

Abstract

Oil and gas operations encounter a very big problem: the normal reactive maintenance that they usually do ends up in catastrophic failures of the equipment, which not only causes the death or injury of the people around but also pollutes the environment and causes a lot of financial losses every year. When pumps and valves break without giving any warning signs, the effects may spread far beyond the oil and gas services and touch the safety of workers, community trust, as well as environmental purity. Maintenance crews have therefore decided to employ digital tools that are able to discover faults at their onset phase. Equipment sensors are nowadays delivering the data to the computers that detect the abnormal patterns weeks before the breakages happen. This progress requires the use of special monitoring, secure networks, and advanced pattern recognition. Facility operators are required to specify the assets that are deserving of advanced monitoring and then create the rightful functional foundation for them. The major change in the oil and gas sector has an impact on the technicians, who, in addition to their mechanical expertise, need to be equipped with digital skills. The most immediate beneficiary of such a move would be the environment, which would be the lucky recipient of a more efficient operation with fewer emissions. The local people are also provided with safety that is in accordance with modern standards, while the engineers are studying the issue of system opacity and the role of human supervision in maintenance decision-making.