Broadcasting Oil and Gas Production Related Emissions in Real-Time and Providing Near-Real Time Source Attribution Using a Mobile Laboratory

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Thursday, 8 January 2015: 9:30 AM
124A (Phoenix Convention Center - West and North Buildings)
Matthew H. Erickson, Houston Advanced Research Center, The Woodlands, TX; and E. P. Olaguer Jr., A. Wijesinghe, J. Colvin, B. Neish, and J. Williams

Understanding emissions related to oil and gas production is becoming increasingly important as the industry continues to grow. Currently there are limited resources for monitoring these emissions, which makes it difficult to quantify the health effects. With modeling and monitoring techniques advancing there are new opportunities to address this problem. The presentation will demonstrate a technique that combines real-time monitoring with micro-scale modeling to provide a means of quantifying oil and gas production site emissions.

A mobile laboratory was constructed utilizing a Ford E-350 passenger van. Named the Mobile Acquisition of Real-Time Concentrations (MARC), the van houses a Proton Transfer Reaction Mass Spectrometer (PTR-MS) to measure important hazardous air toxics in real-time. In combination with meteorological and GPS instrumentation, MARC can provide immediate information about the current air quality. The data is also transferred to an off-site database that in turn broadcasts the information to a website providing real-time information to off-site personnel.

The information on the database is also input into an inverse model, which is used for source attribution. The model is a 3D micro-scale Eulerian forward and adjoint air quality model that uses a 3D digital model of the facility of interest and the Quick Urban and Industrial Complex (QUIC) wind model output along with the real-time observations of MARC to determine the time, place, and quantity of emissions. MARC has been deployed to quantify oil and gas production-related emissions, results from which will be shown to demonstrate the capability to broadcast data in real-time and the potential to provide near real-time source attribution.