David Risk

St. Francis Xavier University , Canada


David Risk is an Associate Professor of Earth Sciences at St. Francis Xavier University in Nova Scotia, Canada. He works closely with industry and regulators to develop gas measurement approaches or programs, and to undertake large-scale studies of methane emission patterns in the North American energy sector.

Short description about presentation:

Aggressive reductions of oil and gas sector methane, a potent greenhouse gas, have been proposed in Canada. Few large-scale measurement studies have been conducted to confirm a baseline. This study used a vehicle-based gas monitoring system to measure fugitive and vented gas emissions across Lloydminster (heavy oil), Peace River (heavy oil/bitumen), and Medicine Hat (conventional gas) developments in Alberta, Canada. Four gases (CO2, CH4, H2S, C2H6), and isotopic 13CCH4 were recorded in real-time at 1 Hz over a six-week field campaign. We sampled 1,299 well pads, containing 2,670 unique wells and facilities, in triplicate. Geochemical emission signatures of fossil fuel-sourced plumes were identified and attributed to nearby, upwind oil and gas well pads, and a point-source gaussian plume dispersion model was used to quantify emissions rates. Our analysis focused exclusively on well pads where emissions were detected >50% of the time when sampled downwind. Emission occurrences and rates were highest in Lloydminster, where 40.8% of sampled well pads were estimated to be emitting methane-rich gas above our minimum detection limits (m = 9.73 m3d1). Of the well pads we found to be persistently emitting in Lloydminster, an estimated 40.2% (95% CI: 32.2%-49.4%) emitted above the venting threshold in which emissions mitigation under federal regulations would be required. As a result of measured emissions being larger than those reported in government inventories, this study suggests government estimates of infrastruc- ture affected by incoming regulations may be conservative. Comparing emission intensities with available Canadian-based research suggests good general agreement between studies, regardless of the measure- ment methodology used for detection and quantification. This study also demonstrates the effectiveness in applying a gaussian dispersion model to continuous mobile-sourced emissions data as a first-order leak detection and repair screening methodology for meeting regulatory compliance.