Sensors and Process Monitoring Models Applied for Pre-salt Petroleum Extraction Platforms Applications

This work presents a modelling methodology for sensors and equipment condition monitoring developed during a research project to enhance dependability of pre-salt petroleum extraction platforms. The methodology aims to improve the capability of the auto-associative models applied for sensors monitoring in the last decades in nuclear power plants, chemical industry, refineries, gas transport and processing plants. However, actual operation problems or fault in equipment also may lead to false measurement error detection. This problem observed in the previous applications motivated the development of the improved method able to detect measurement errors and fault conditions in the process or equipment. This improvement has been obtained adding data (real or simulated) of the different conditions of operation, including the fault conditions (undesired data in the previous methodology). Therefore, the models become able to make accurate sensor estimation, even under fault conditions in the monitored process, and they also give a proper fault diagnoses about the measurement instruments and the process reducing false alarms compared to the traditional approaches. Also, some modelling challenges were observed during the development such as optimization of parameters, memory size and computing complexity. The methodology is demonstrated using simulated a process of a Petroleum Platform application. The achieved results showed a possible methodology to improve or replace the traditional approaches in the past application.