Keeping pace with the industry digital transformation accelerated by the 2020 pandemic, PETRONAS sees the opportunities of sustaining the forward momentum of its Pipeline Integrity Management System (PIMS) digitalization deployment as it becomes more deeply integrated in day-to-day operation. After two years of utilizing digital PIMS, the platform services have matured, and PETRONAS is able to reap the benefits of having a centralized digital platform for pipeline related information. The aspiration for the pipeline digital PIMS is to give near real-time insight of overall pipeline condition and risk, and for operators to perform proactive actions to eliminate downtime by identifying early warnings of failure. The immediate value of digital PIMS implementation was having the legacy data being contextualized and transformed into structured data, which is the backbones for descriptive, predictive and prescriptive big data analytics. The platform was devised as such that integration with Enterprise application for data ingestion was made easier. As of to-date, digital PIMS is able to pull and push data to PETRONAS Enterprise Resource Planning tool, geographic information system (GIS) supported software, as well as operational monitoring real-time data through Supervisory Control and Data Acquisition (SCADA) system. In addition to conventional deterministic fitness for service analytics modules, the platform is also currently integrated with analytics modules, primarily for onshore and offshore pipeline threats such as internal corrosion, free span, geohazard and damage from third party. By implementing predictive analytics approach to assess pipeline threats, it will assist stakeholders and operators in optimizing inspection campaign and rectification, thus ultimately reduce OPEX throughout pipeline remaining lifecycle.
One of the challenges in pipeline operational pigging in multiphase pipeline is high Pig Generated Volume (PGV) liquid slug that arrives at the end of the pipeline receiving system. This causes process upset at the receiving plant, which in turn causes production deferment each time pigging activity is performed. Introduction of bypass pig for cleaning activity is one of the best solutions to manage the arriving liquid slug at the receiving end to allow ample time for the plant to control the arriving slug and evacuate liquid without initiating a process upset. Usually to design the bypass pig would require specific hydraulic pipeline simulation with expertise and experience personnel to perform the assessment. Meanwhile, pipeline operator are also dealing with a fluctuating gas flowrate, temperature and pressure during pigging operation which effect the selection of bypass pig design. There are records of bypass pig stalled which happened as a result of not considering the variation of these input variables. This paper elaborates alternative calculation methodology using Monte Carlo probabilistic method to assess input variables via a spreadsheet, prior to determining the final bypass pig design. Groot (2015) provide a detailed explanation on bypass pig calculation using mass balanced approach to determine the bypass pig speed based on specified bypass opening. To determine the bypass pig speed, the gas velocity through bypass opening is subtracted from gas mixture velocity. For this case study, bypass pig with disc type is used. The minimum and maximum value for input parameter is used in the calculation using RANDOM functions in the spreadsheet. The calculation is repeated for 10,000 iterations to get the different result based on random range of operating parameter that has been set earlier. The calculation boundary is determined whereby for this case is the bypass pig speed. Result which fall within the boundary will determine the success rate of the bypass pig design, without pig stall or over speed. Using Monte Carlo probabilistic method, the optimum bypass pig opening could be determined to avoid over speed or pig stall. Probabilistic method enables the user to analyze using descriptive statistic ie. minimum, maximum or average pig speed from various conditions. In conclusion, probabilistic method has significantly help the user to assess the bypass pig speed with fluctuation of operating parameters using simple spreadsheet.
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