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Preprints
https://doi.org/10.5194/acp-2020-337
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-337
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 May 2020

Submitted as: research article | 15 May 2020

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This preprint is currently under review for the journal ACP.

Quantifying methane emissions from Queensland's coal seam gas producing Surat Basin using inventory data and an efficient regional Bayesian inversion

Ashok K. Luhar1, David M. Etheridge1, Zoë M. Loh1, Julie Noonan1, Darren Spencer1, Lisa Smith2, and Cindy Ong3 Ashok K. Luhar et al.
  • 1CSIRO Oceans and Atmosphere, Aspendale, Victoria 3195, Australia
  • 2Katestone Environmental Pty. Ltd., Milton, QLD 4064, Australia
  • 3CSIRO Energy, Kensington, WA 6152, Australia

Abstract. Methane emissions across Queensland’s Surat Basin, Australia, result from a mix of activities, including the production and processing of coal seam gas (CSG). We measured methane concentrations over 1.5 years from two monitoring stations established 80 km apart on either side of the main CSG belt located within a study area of 350 × 350 km2. Coupling bottom-up inventory and inverse modelling approaches, we quantify methane emissions from this area. The inventory suggests that the total emission is 173 × 106 kg CH4/yr, with grazing cattle contributing about half of that, cattle feedlots 25 %, and CSG Processing 8 %. Using the inventory emissions in a forward regional transport model indicates that the above sources are significant contributors to methane at both monitors. However, the model underestimates approximately the highest 15 % of the observed methane concentrations, suggesting underestimated or missing emissions. An efficient regional Bayesian inverse model is developed, incorporating an hourly source-receptor relationship based on a backward-in-time configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical the gridded bottom-up inventory emissions across the domain with an uncertainty of 3 % of the averages best describes the observed methane. Having only two stations is not adequate at sampling distant source areas of the study domain, and this necessitates a small prior uncertainty. This inverse setup yields a total emission that is very similar to the total inventory emission. However, in a subdomain covering the CSG development areas, the inferred emissions are 33 % larger than those from the inventory.

Ashok K. Luhar et al.

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Ashok K. Luhar et al.

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Short summary
With the sharp rise of coal seam gas (CSG) production in Queensland’s Surat Basin, there is much interest in quantifying methane emissions from this area. For this purpose, we use both bottom-up and top-down (i.e. inverse) approaches. An efficient regional Bayesian inverse model is formulated, and applied using measured hourly in-situ methane concentrations. The inverse model suggests that the methane emissions from the CSG-dominated area are 33 % higher than the bottom-up inventory estimates.
With the sharp rise of coal seam gas (CSG) production in Queensland’s Surat Basin, there is...
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