Abstract

Safety and reliability of the assets in the Oil and Gas sector is considered critical to maximize availability and minimize downtime in production and operations. The industry, due to ageing assets, are re-visiting the maintenance planning of critical equipment through Reliability-Centered Maintenance (RCM). The RCM process comprises of the hierarchical structuring of technical objects according to their functional locations and further dividing them into their maintainable items and components. Generally, a criticality matrix is developed that would identify the highly critical assets based on criteria such as cost, production, safety, and environment. The Failure Modes and Effects Analysis (FMEA) is then performed for the identified functions, functional failures, and likely failure modes of each component of the asset. The failure modes can be identified from first principles and historical failure data from the asset owner or databases such as the Offshore Reliability Database (OREDA). The failure modes, mechanisms and causes are defined in accordance with International Standards such as ISO14224. The failure characteristics such as the hidden or evident nature of the failure modes, along with the occurrence of the failure within the bathtub curve, would help in deciding the right maintenance strategies such as condition-based, fixed-time, fault-finding intervals, run to failure or redesign. The maintenance tasks are then developed as a mitigation against each failure mode. Efficient and effective asset management strategies are achieved through the allocation of resources such as the labor, tools and materials required to execute the maintenance task. The maintenance tasks are grouped together based on the maintenance time intervals and the resulting maintenance plans are fed into the Computerized Maintenance Management System (CMMS) to manage the maintenance process and track the implementation of the maintenance tasks on site. The maintenance time intervals are allocated mainly based on the current industrial practices and may be qualitative in nature. This paper aims to quantitatively determine the right maintenance intervals by analyzing the historical failure data and determining the Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR). Cost and reliability analysis of the maintenance scenarios based on best industry practices and statistical analysis is determined. This helps with the decision-making process by improving the maintenance task intervals based on planned and unplanned maintenance downtime. A case study of an asset from the Oil and Gas sector is provided for demonstrating the proposed approach.

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