Experimental Modelling of Black Carbon Emissions from Gas Flares in the Oil and Gas Sector

Flares in the upstream oil and gas (UOG) industry are an important and poorly quantified source of black carbon (BC) emissions and may be a dominant source of black carbon deposition in sensitive Arctic regions (Stohl et al. 2013).  Accurate estimation of flare BC emissions to support informed policy decisions, accurate climate modeling, and new international reporting regulations under the Gothenburg protocol is a critical challenge.  To date few studies have focussed on the primarily buoyancy-dominated turbulent non-premixed flames typical of upstream oil and gas flares, such that existing emission factor models are highly uncertain (see (McEwen and Johnson 2012)).  Although recent progress has been made in measuring black carbon from flares in the field (e.g. (Conrad and Johnson 2017; Johnson et al. 2013), data have also shown that emissions of individual flares may vary by more than 4 orders of magnitude. 

The objective of the current study is to develop a robust data-backed model to predict black carbon emissions from flares considering variations in flare gas composition, flow rates, and stack diameters.  Laboratory measurements of black carbon (soot) for a range of turbulent non-premixed jet diffusion flames of up to 3 m in length were performed at the Carleton University Flare Facility in Ottawa, Canada.  Two hundred and thirty cases spanning five flare stack diameters (25.4 to 76.2 mm), exit velocities from 0.16 to 15.15 m/s, and a broad range of industrially-relevant multicomponent (C1-C7 hydrocarbons, CO₂, N₂) flare gas compositions were studied.  Emissions were captured in a large (~3.1 m diameter) sampling hood and forwarded to gas- and particulate phase analyzers. 

Black carbon concentrations were measured via a Sunset Labs thermal-optical instrument using the OCECgo software tool (Conrad and Johnson 2019) to quantify uncertainties via Monte Carlo analysis.  BC yields were subsequently calculated using a mass-balance methodology (Corbin and Johnson 2014).  Variability in BC yield was well-predicted by an empirical model incorporating both the aerodynamic and chemistry effects.  For this range of conditions, it was observed that primary independent variables (such as exit velocity and higher heating value) act as reasonable surrogates for sooting propensity.  Further experiments are underway to test the proposed model over a broader range of conditions.  However, results to date represent a significant advance in our ability to predict black carbon emissions from flares.