Atmospheric Tomography as a Tool for Quantification of CO2 Emissions from Potential Surface Leaks: Signal Processing Workflow for a Low Accuracy Sensor Array

AbstractAtmospheric tomography is a monitoring technique that uses an array of sampling sites and a Bayesian inversion technique to simultaneously solve for the location and magnitude of a gaseous emission. Application of the technique to date has relied on air samples being pumped over short distances to a high precision FTIR Spectrometer, which is impractical at larger scales. We have deployed a network of cheaper, less precise sensors during three recent large scale controlled CO2 release experiments; one at the CO2CRC Ginninderra site, one at the CO2CRC Otway Site and another at the Australian Grains Free Air CO2 Enrichment (AGFACE) facility in Horsham, Victoria. The purpose of these deployments was to assess whether an array of independently powered, less precise, less accurate sensors could collect data of sufficient quality to enable application of the atmospheric tomography technique. With careful data manipulation a signal suitable for an inversion study can be seen. A signal processing workflow based on results obtained from the atmospheric array deployed at the CO2CRC Otway experiment is presented

Bayesian atmospheric tomography for detection and quantification of methane emissions : application to data from the 2015 Ginninderra release experiment

Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and assumptions that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point-and path-sampling instruments. The Bayesian framework is designed to account for uncertainty in the parameterisations of measurements, the meteorological data, and the atmospheric model itself when performing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type and meteorological conditions. From all the inversions we conducted across the different instrument groups and release-rate periods, our worst-case median emission rate estimate was within 36% of the true emission rate. Further, in the worst case, the closest limit of the 95% credible interval to the true emission rate was within 11% of this true value

The Ginninderra CH4 and CO2 release experiment: An evaluation of gas detection and quantification techniques

A methane (CH4) and carbon dioxide (CO2) release experiment was held from April to June 2015 at the Ginninderra Controlled Release Facility in Canberra, Australia. The experiment provided an opportunity to compare different emission quantification techniques against a simulated CH4 and CO2 point source release, where the actual release rates were unknown to the participants. Eight quantification techniques were assessed: three tracer ratio techniques (two mobile); backwards Lagrangian stochastic modelling; forwards Lagrangian stochastic modelling; Lagrangian stochastic (LS) footprint modelling; atmospheric tomography using point and using integrated line sensors. The majority of CH4 estimates were within 20% of the actual CH4 release rate (5.8 g/min), with the tracer ratio technique providing the closest estimate to both the CH4 and CO2 release rates (100 g/min). Once the release rate was known, the majority of revised estimates were within 10% of the actual release rate. The study illustrates the power of measuring the emission rate using multiple simultaneous methods and obtaining an ensemble median or mean. An ensemble approach to estimating the CH4 emission rate proved successful with the ensemble median estimate within 16% for the actual release rate for the blind release experiment and within 2% once the release rate was known. The release also provided an opportunity to assess the effectiveness of stationary and mobile ground and aerial CH4 detection technologies. Sensor detection limits and sampling rates were found to be significant limitations for CH4 and CO2 detection. A hyperspectral imager\u27s capacity to image the CH4 release from 100 m, and a Boreal CH4 laser sensor\u27s ability to track moving targets suggest the future possibility to map gas plumes using a single laser and mobile aerial reflector

Atmospheric tomography: a bayesian inversion technique for determining the rate and location of fugitive emissions

A Bayesian inversion technique to determine the location and strength of trace gas emissions from a point source in open air is presented. It was tested using atmospheric measurements of N2O and CO2 released at known rates from a source located within an array of eight evenly spaced sampling points on a 20 m radius circle. The analysis requires knowledge of concentration enhancement downwind of the source and the normalized, three-dimensional distribution (shape) of concentration in the dispersion plume. The influence of varying background concentrations of ~1% for N2O and ~10% for CO2 was removed by subtracting upwind concentrations from those downwind of the source to yield only concentration enhancements. Continuous measurements of turbulent wind and temperature statistics were used to model the dispersion plume. The analysis localized the source to within 0.8 m of the true position and the emission rates were determined to better than 3% accuracy. This technique will be useful in assurance monitoring for geological storage of CO2 and for applications requiring knowledge of the location and rate of fugitive emission

Table 4. Trace gas concentrations from samples taken at Ginninderra

We assess the performance of an inverse Lagrangian dispersion technique for its suitability to quantify leakages from geological storage of CO2. We find the technique is accurate ((QbLS/Q)=0.99, sigma=0.29) when strict meteorological filtering is applied to ensure that Monin–Obukhov Similarity Theory is valid for the periods analysed and when downwind enrichments in tracer gas concentration are 1% or more above background concentration. Because of their respective baseline atmospheric concentrations, this enrichment criterion is less onerous for CH4 than for CO2. Therefore for geologically sequestered gas reservoirs with a significant CH4 component, monitoring CH4 as a surrogate for CO2 leakage could be as much as 10 times more sensitive than monitoring CO2 alone. Additional recommendations for designing a robust atmospheric monitoring strategy for geosequestration include: continuous concentration data; exact inter-calibration of up- and downwind concentration measurements; use of an array of point concentration sensors to maximise the use of spatial information about the leakage plume; and precise isotope ratio measurement to confirm the source of any concentration elevations detected

Atmospheric Tomography: A Bayesian Inversion Technique for Determining the Rate and Location of Fugitive Emissions

A Bayesian inversion technique to determine the location and strength of trace gas emissions from a point source in open air is presented. It was tested using atmospheric measurements of N₂O and CO₂ released at known rates from a source located within an array of eight evenly spaced sampling points on a 20-m radius circle. The analysis requires knowledge of concentration enhancement downwind of the source and the normalized, three-dimensional distribution (shape) of concentration in the dispersion plume. The influence of varying background concentrations of ∼1% for N₂O and ∼10% for CO₂ was removed by subtracting upwind concentrations from those downwind of the source to yield only concentration enhancements. Continuous measurements of turbulent wind and temperature statistics were used to model the dispersion plume. The analysis localized the source to within 0.8 m of the true position and the emission rates were determined to better than 3% accuracy. This technique will be useful in assurance monitoring for geological storage of CO₂ and for applications requiring knowledge of the location and rate of fugitive emissions