Abstract
Inspection and maintenance decisions are key elements for assuring the technical integrity of oil and gas (O&G) production plants. In this context, the offshore industry is facing a challenge in replacing experienced personnel with new recruitments. The issue is further exacerbated when the job responsibilities involve high risk related decisions. Therefore, it is important to replace the human involvement in decision making processes with intelligent systems. The methods developed in operation research and/or the hybrid systems such as neurofuzzy methodologies provide a backbone for developing such systems. As the personnel working in the inspection planning deals with large amount of data from different data sources, it is vital to develop a mechanism to integrate these data to make the optimum decision. This paper proposes a framework for the mechanization of inspection planning and corresponding decision making processes, focusing on static mechanical equipment in offshore production plants.
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