INDUSTRY FOCUS OIL & GAS USING DATA TO UNLOCK PLANT POTENTIAL


Dr Yanfeng Liang, mathematician at TÜV SÜD National Engineering Laboratory, explains the advantages of condition-based calibration compared to time-based calibration


D


ata is used by metering technicians and commissioning engineers in the


oil and gas industry for maintenance and quick checks, to ensure that devices are performing as expected. Many facilities also use the diagnostic values for simple range checking to indicate when a given parameter has drifted out of acceptable conditions. However, the combined costs of meter calibration, pipe fitting, electrical isolation/connection and facility downtime can be significant. This has led to a financial and operational desire to move towards a system which embraces condition-based calibration (CBC) as opposed to time-based calibration (TBC) on devices such as flow meters. Traditionally, there has been a reliance


on the TBC method which may result in facility operations being stopped unnecessarily simply to calibrate a flow meter which has not deviated from its required operating parameters. Conversely, it is also possible for a meter to have deviated from its expected performance envelope, but as it is not due for recalibration, the resulting fluid measurement errors may have financial consequences for the facility operators.


DEEP INSIGHTS Condition-based Monitoring enables real- time analysis of fluid conditions while quantifying the uncertainty of predicted outcomes, and therefore gives deep insights into the status of operational systems. This may also include anomaly detection or highlighting potential problems in the system before equipment is damaged. A better understanding of the data also leads to a more efficient decision-making process (either automated or human) in areas such as production optimisation and custody transfer. The resulting CBC schedule has the


potential to reduce operating costs by allowing facilities to develop more dynamic operating patterns that are based on continuous, automated, diagnostic analysis of facility and meter


28 OCTOBER 2020 | PROCESS & CONTROL


performance. By logging key meter diagnostic values in tandem with standard device outputs and comparing them to known baseline conditions, it is possible to determine whether a flow meter is operating within specification. Consequently, CBC allows old machines and infrastructure to be operational for a longer period which, in turn, leads to the extension of Remaining Useful Life (RUL). Also, with enough historical information on a specific device, it is possible to predict data calibration drift over time.


EFFECTIVE PLANNING The source of the data is specific to the processes, but the objective for all remains the same - to use statistical modelling techniques to develop a toolset that eventually predicts performance based on live and historical data. However, if CBC is implemented in place of TBC, then maintenance resource planning becomes more challenging due to the irregular intervals, and so predictive capability becomes crucial to allow effective planning to continue. Factors that are gradually increasing the


uptake of CBC-based facility maintenance patterns are the continued growth and adoption of cloud-based computing and data storage, as well as affordable computing power which is required for complex modelling and prediction. The standardisation of digital communication protocols, as well as individual manufacturers supporting the integration of their devices into cross platform packages, have also allowed for a number of application-specific software solutions to be developed that support CBC facility operation. The principle of CBC and monitoring is a


component of a much larger concept, broadly referred to as ‘Digital Oilfield’. The


Research will help end- users to build confidence in CBC. For example, flow laboratories are currently researching correlations between the data output from field devices and their operational efficiency


overall aim of this is to optimise facility operating costs by streamlining areas such as maintenance, staff scheduling, production and data analysis. The exact parameters of a ‘Digital Oilfield’ system are influenced by the specifics of the facility to be monitored. The specification and commissioning of


Dr Yanfeng Liang, Mathematician at TU ̈D National


SU Engineering Laboratory ̈V


such a system requires an in-depth understanding of the facility’s electronic, electrical and mechanical design, as well as its normal operating requirements and capabilities. When predictive information is initially generated it should be validated by staff with relevant knowledge and experience before key decisions are made on the data. Over time, after multiple tuning iterations, confidence in the data is built-up, so the facility can start to adopt efficient and intelligent decision-making, as opposed to a regimented and potentially inefficient one.


BUILDING CONFIDENCE Research is currently underway in multiple industry and academic sectors, with the aim of helping end-users build confidence in these types of system. For example, our flow laboratories are currently researching correlations between the data output from field devices and their operational efficiency. Additionally, many companies are in the


process of analysing the historical big data sets associated with the specifics of their operation. This is not limited to the oil and gas industry. Sectors such as food production, retail, automotive etc., are all undertaking ‘digitisation’ strategies, with the aim of getting to grips with the subtleties and unrealised potential in the historical and live data which they hold.


TÜV SÜD NEL www.tuvsud.com/en-gb/nel


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