An integrated qualitative trend analysis approach to identify process abnormalities: A case of oil export pumps in an offshore oil and gas production facility

Abstract

Oil and gas production can be largely benefited by minimizing unwanted production losses. This can be done by effective identification of system anomalies and faults. In standard control systems these abnormalities can be observed as gradually deviating trends from the norms. Available tools for monitoring these trends, in some cases, may not be enough to reveal hidden faulty features. In order to interpret these changes accurately, measured data must be visualized as a combination of multiple sensor signals within a particular domain. This article suggests an approach to effectively utilize integrated data from multiple sources, and defines a set of 12 fault features. The approach, in principle, encodes real plant data in the form of logical IF-THEN rules in Microsoft Excel. Confidence values are set based on these interpretations to differentiate between normal and abnormal conditions exhibited by the system. This is to provide an opportunity for the process and maintenance engineers to effectively identify the equipment's health based on the early identification of developing abnormalities.