Dispersion based Recurrent Neural Network Model for Methane Monitoring in Albertan Tailings Ponds

TL;DR

This paper presents a physics-constrained machine learning model that accurately estimates methane emissions from oil sands tailing ponds, providing crucial data for environmental impact assessment and mitigation strategies.

Contribution

The study introduces a novel dispersion-based recurrent neural network model that integrates mechanistic and real-time weather data for methane emission estimation in oil sands tailings ponds.

Findings

Active ponds emit 950-1500 tonnes of methane annually.

Abandoned ponds may emit up to 1000 tonnes per year.

Reducing emissions by 12% could restore methane levels to 2005 benchmarks.

Abstract

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle basins commonly known as oil sands tailing ponds. In order to determine the methane emitting potential of these tailing ponds and have future methane projections, we use real-time weather data, mechanistic models developed from laboratory controlled experiments, and industrial reports to train a physics constrained machine learning model. Our trained model can successfully identify the directions of active ponds and estimate their emission levels, which are generally hard to obtain due to data sampling restrictions. We found that each active oil sands tailing pond could emit between 950 to 1500 tonnes of methane per year, whose environmental impact is equivalent to carbon dioxide emissions from at least 6000 gasoline powered vehicles. Although abandoned ponds are often presumed to have insignificant emissions, our findings indicate that these ponds could become active over time and potentially emit up to 1000 tonnes of methane each year. Taking an average over all datasets that was used in model training, we estimate that emissions around major oil sands regions would need to be reduced by approximately 12% over a year, to reduce the average methane concentrations to 2005 levels.