Oil IT Journal went downstream last month, attending the 19th Belsim User Meeting in Brussels. Before the meeting kicked off, we chatted with Belsim founder, president and chairman Boris Kalitventzeff who explained how the company originated. As the name implies, Belsim is a Belgian software house with its origins in the simulation field. Kalitventzeff’s early work at Liège University saw cooperation with Houston-based academics on process modeling. It was clear early on that matching models with real data from refining and other industrial processes held some surprises. It was frequently impossible to get a decent match of both model inputs and outputs. There appeared to be structural problems with recorded process data.
It was then that Kalitventzeff realized that instead of using modeling to simulate a process, it could be put to better use in the first instance to validate data prior to its use in other applications like monitoring or, indeed in simulation and forecasting. This represented an opportunity for the development of a suite of tools leveraging full physics modeling to validate data in an industrial plant. The technique was first tested in a naphtha plant where it proved to be very successful.
A second industrial user heard of this success and asked to use the toolset. But by then, Kalitventzeff, in true academic fashion, had gone on to work on other research topics. When the second plant owners complained about the lack of attention, the university and Kalitventzeff realized that this was actually an opportunity to start a company.
‘We started out with simulation, but the real niche we found was data validation.’ Today Belsim develops and markets its flagship Vali data validation engine to process companies including refineries and, increasingly, upstream users. Vali, now in release 5.0, can act as a pre-processor for process models—but also simply to provide operators with reliable plant data for maintenance and operations.
One Middle East operator described the role of Vali as at the core of its new information system for a greenfield refinery. The refinery information system supports multiple business applications across the supply chain, planning and scheduling, optimization and safety. The ‘truly integrated’ system, built atop of Microsoft BizTalk, displays physical and logical flows in its GUI, stores and ‘historizes’ model runs. Vali provides cleansed data to SAP and other applications.
Vali was selected because it provides both reconciliation and mass/energy balance in a single tool. Data validation and reconciliation is performed across 7000 tags and three ‘very large full refinery models’ for daily, monthly and offline DVR. Data comes from lab measurements, flow meters, oil movement and manual input from Excel. Output goes to Vali’s reporting tools. Reconciled values are captured to the data historian and input to SAP and the information system dashboards.
The system is used to identify faulty meters and to provide early warning of instrument and equipment failure. The offline models act as a pre-processor to provide validated input to simulators. Vali is a key component of the system and provides plant-wide data validation of daily mass and energy balance of thousands of refinery processes and tanks.
Quirinius van Dorp decribed Dana Petroleum’s use of Vali in upstream data reconciliation. Dana operates the North Sea P11b De Ruyter Platform in the Dutch North Sea. Belsim Vali was used here to provide ‘virtual flow metering’ leveraging an ‘abundance’ of upstream data. The VFM provides a best estimate of true flow for use in well integrity and process optimization base on thermodynamics and mass balance. The system also allows safety systems to ‘kick in quick’ when required. In 2010 Dana was acquired by the Korean national oil company and undertook a major program to deploy multi phase flow meters (MPFM) for custody transfer on its new Medway project. This was problematical as the MPFMs did not prove reliable. Meanwhile the project itself was increasing in complexity as the oil rim was developed requiring new separators and a desalter. Medway is now a very large process with 1200 tags. Prior to Vali all this was ‘managed’ in a ‘monstrous’ spreadsheet which proved hard to maintain and check.
Vali now provides a graphic interface showing process flow and instrumentation diagrams along with validated models. It is easy to see where tag values do not fit in thermodynamic balance and drill down to get a list of all parameters involved in a calculation. Red boxes show data that is out of range—indicative of a failed meter.
van Dorp concluded that Belsim’s software has proved more reliable than the MPFM, whose data was found to be plagued by spurious spikes and erroneous flow rates. Vali also satisfies the contractual obligation to use all available data to arrive at the best result. This is done by combining temperature measurements, choke equations, mass, energy balance (and a lot of other parameters) every hour. Vali takes under one minute to process the data. Use of the model has helped Dana decide which meters to buy and where to put them. The software also detects failing meters before they go down. Vali models are static, values captured during transient events may not be accurate but they are usually acceptable. In all events, models need to be checked throughout the life of field.
Another use case outlined Vali’s use in energy performance management at a Middle East plant. Raw measurements of energy use are often meaningless and/or show a systematic bias. Vali is deployed in a SharePoint portal that allows operators to drill down into the plant structure and pinpoint major deviations. Cleaned data is saved in a SQL Server database. Data can be rolled-up into a site-wide energy use metric using the US EPA Energy Star grading. This has demonstrated a 5-8% energy saving from the software. KPIs are shared with users (and the CEO) to create incentives for further savings.
Antonio Martino presented Hess’ use of virtual metering on its Equatorial Guinea Ceiba project. Here well tests were providing uncertain rate allocations and formation gas/oil ratios were hard to estimate because of flow line gas lift. Further uncertainty came from gauge reliability and calibration. Vali was used to build a virtual metering system (a.k.a. soft sensing) that has allowed for accurate volume reporting and reduced well testing downtime. The model has matched well test rates within 5%. More from Belsim.
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