Yokogawa Electric’s subsidiary KBC has reached an agreement to partner with Inpex and Heriot-Watt University to launch a new research project to help mitigate mercury-related risk in the processing of oil and gas (O&G) reservoir fluids.
The research project will be co-funded by the Oil & Gas Innovation Centre (OGIC).
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As part of the project, KBC will work with Inpex and Heriot-Watt University to generate new data for enhanced prediction of mercury solubility in O&G processing.
The collaboration will leverage the companies’ test and measurement facilities and techniques, as well as thermodynamic modelling and process simulation software.
Mercury is a highly corrosive metal and a common feature in most reservoir fluids.
The accumulation of the element in pipelines or operating units such as heat exchangers or separators can pose a threat to their structural integrity.
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By GlobalDataThis leads to conservative approaches in process facility design and construction, with excessive design margins that could subsequently result in increased project capital costs and operating expenditures.
The project aims to improve the understanding of mercury distribution through hydrocarbon processing systems in order to optimise project economics.
Expected to be completed by March 2020, the research project also aims to reduce exposure to personnel and the environment.
KBC CEO Andy Howell said: “Mercury is a well-recognised yet under-researched challenge facing design, operations and maintenance teams across the upstream oil and gas sector.
“I’m confident that the breadth and depth of resources and capabilities of KBC, Heriot-Watt University and Inpex will yield valuable insights for addressing mercury-related issues across the industry.”
It is estimated that operators spend around 5% of the total cost of oil and gas production facilities on mercury removal.
To address the industry-wide issue, the parties will focus on the improved prediction of mercury accumulation across facilities to facilitate more effective selection and right-sizing of equipment within safety margins.
The enhanced prediction will also enable efficient operation and maintenance.
