Implementation of Online Monitoring System of Crude Oil Pipeline Surface Facility to Deal with Data Mining Environment Technology

Crude oil pipeline surface facility in petroleum industries often has many massive problems. One of them is the crude oil blocking of the pipelines which called as congeal. To monitor events in the pipeline system, analog pressure gauges conventionally are installed. The conventional pressure gauges is able to record events that took place, but is not able to carry out event prediction in the future. Moreover, the data is very limited. This paper aims to report the implementation of an online monitoring system in the crude oil pipeline system to predict and control the congeal phenomenon. The pressures at the remote part of the pipeline system are monitored using pressure transmitter 3051 series and send the data automatically to the data center. Complemented with other data such as temperature and weather conditions, the data obtained by the online monitoring system can be analyzed using a data mining algorithm, and then pipeline pressure on the following days could be predicted. Therefore, anticipated actions could be planed if the pressure prediction for the following days show abnormal conditions to prevent congeal. As discussions, data acquired from the online monitoring system are presented and an example of a data mining method to predict the pressure are also explained in this paper. With online monitoring and historical data, the analysis method has also begun to shift from the initial analysis using a single discipline science from piping systems such as fluid mechanics, to change into data mining analysis. As a suggestion for the continuation of this research, it is necessary to continue with data analysis using widely available data mining algorithms to obtain suitability that can be applied to monitor flow assurance conditions in the pipelines and provide signals or alarms in the event of abnormal phenomena including congeal. © 2021 IEEE.