The document describes two controlled release experiments of methane and carbon dioxide at the TOTAL Anomaly Detection Initiatives site in Lacq, France. Mobile and fixed-point sensors measured gas concentrations during the releases. New atmospheric inversion methods were developed to estimate the location and rate of the releases using the measurement data and a Gaussian plume dispersion model. One method used mobile transect measurements to estimate the location and rate of a brief release by minimizing differences between observed and modeled plume areas and wind directions. A second method used fixed sensors and a least-squares approach over time windows and wind sectors to localize and quantify longer releases. The inversions aimed to accurately determine release rates and locations from the controlled experiments.

1. TRACE
TRACING CARBON EMISSIONS
Programme ANR Chaire Industrielles - Editions 2020
Local-scale atmospheric inversion for the estimation of the
location and rate of CH4 and CO2 controlled releases using
mobile and fixed-point measurements
Pramod Kumar1 Grégoire Broquet1
Christopher Caldow1
Olivier Laurent1
Camille Yver-Kwok1
Ford Cropley1
Sara Defratyka1
Susan Gichuki1
Thomas Lauvaux1
Rodrigo Rivera1
Bo Zheng1
Guillaume Berthe2
Frédéric Martin2
Sonia Noirez2
Olivier
Duclaux3
Catherine Juery3
Caroline Bouchet4
Michel Ramonet1
Philippe Ciais1
1
LSCE, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
2
IFP Energies nouvelles-Géoscience, 92852 Rueil-Malmaison Cedex, France
3
TOTAL Laboratoire Qualité de l’Air (LQA), 69360 Solaize Cedex, France
4
SUEZ-Smart & Environmental Solutions, Tour CB21/16 place de l’Iris, 92040, La Défense, France
September 16, 2020
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 1 / 18

2. Introduction
Context
One of the major challenges in mitigating GHG emissions from oil & gas
facilities: accurate monitoring (detection, localization, and quantification)
of fugitive emissions
Need to develop and apply different techniques based on atmospheric
measurements to accurately localize and quantify its emissions
TRACE is a project aiming at developing different measurements and
inverse modeling techniques for monitoring of the emissions
Two campaigns (TADI-2018 and TADI-2019) of CH4 and CO2 controlled
release experiments
Objectives: accurate estimates of TADI-2018/2019 release rates
and locations
Atmospheric GHG concentration measurements by stationary or/and
mobile sensors
Inversions using mobile or/and fixed-point measurements and a Gaussian
plume dispersion model
New inversion frameworks adapted to the specific measurement
conditions and configurations
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 2 / 18

3. TOTAL Anomaly Detection Initiatives (TADI) site (Lacq, southwestern France)
Objective: Development and testing of different emission quantifying technologies
to manage the risks associated with any gas leak at industrial facilities
Various realistic release scenarios using old infrastructure from gas facilities:
Different release heights, angles, orifices, obstructions etc.
Release points: Drilled plugs, Pipes rack corrosion, Flange, Valve, Control boxes,
horizontal or vertical tubing, horizontal or vertical piping, Manhole, etc.
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 3 / 18

4. TADI-2018 campaign (15-19 October, 2018): Focus on safety testing
50 releases of CH4 or CO2
A wide range of release rates: 0.1 g/s to 200 g/s
Short durations: typically 4-8 minutes
Released heights: 0.39 m to 6 m above the ground
Mobile near-surface measurements (in Collaboration with IFPEN)
Continuous measurements obtained around TADI
Suite of gas analyzers (Picarro G2203 (CH4), Picarro G2401 (CO2) )
GPS coordinates of measurements (AIRMAR)
Metek Sonic 3D sonic anemometer (TOTAL) at 10 m height
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 4 / 18

5. Measurements: Transects paths, spatial distribution, concentration time series
An example of mobile near-surface measurements (TADI-2018)
50 0 50 100 150 200
x (m)
100
80
60
40
20
0
20
y(m)
Source
Start
End
0.05
0.10
0.15
0.20
0.25
0.30
0.35
CH4(ppm)
x (m)
0
100
200
y (m)
100
75
50
25
0
25
CH4(ppm)
0.05
0.10
0.15
0.20
0.25
0.30
0.35
14:10:00
14:11:00
14:12:00
14:13:00
14:14:00
14:15:00
14:16:00
14:17:00
14:18:00
Time (UTC)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
CH4(ppm)
release-2
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 5 / 18

6. TADI-2019 campaign (Oct 2-10, 2019 ): Focus on environmental monitoring
41 releases mainly CH4, sometimes mixed with CO2, C2H6, C3H8
Release rates: 0.4 to 150 g CH4/s, mainly 0.5 - 5 g CH4/s
Higher release durations: 25-75 minutes
Mobile + Fixed-point measurements
Air intakes at 16 tripods around platform, 6-7 active simultaneously
Inlet heights: 2.5 to 3.5 m
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 6 / 18

7. Wind and CH4 concentration at active tripods (release-1: Qs = 10 g CH4/s)
An example of fixed-point measurements (TADI-2019)
09:0008:15 08:20 08:25 08:30 08:35 08:40 08:45 08:50 08:55 09:05 09:10
Time (UTC)
2
3
4
Ur(m/s)
250
260
270
280
(°)
2.0
2.1 Tripod-4
0
500 Tripod-3
0
500
CH4(ppm)
Tripod-2
0
200 Tripod-1
0
50 Tripod-16
09:0008:15 08:20 08:25 08:30 08:35 08:40 08:45 08:50 08:55 09:05 09:10
Time (UTC)
1.950
1.960
1.970 Tripod-15
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 7 / 18

8. Inversion methods: Gaussian plume dispersion model
For a continuous point release with reflective ground surface:
C(x, y, z) =
Qs
2πσyσzU
exp −
(y − ys)2
2σ2
y
exp −
(z − zs)2
2σ2
z
+ exp −
(z + zs)2
2σ2
z
(1)
where C(x, y, z) = average concentration at a receptor (x, y, z),
Qs = emission rate of a point source located at (xs, ys, zs),
U = mean wind speed at the height of a release, and
σy and σz = dispersion parameters in (y) and (z) directions, respectively.
Dispersion parameters:
Based on the turbulence measurements (Gryning et al., 1987):
σy = σvt 1 +
t
2Ty
−1
(2a)
σz = σwt 1 +
t
2Tz
−1
(2b)
where σv, σw = turbulent velocity fluctuations in lateral and vertical directions,
t(= x/U) = is the travel time,
Ty and Tz = the Lagrangian time scales in y- and z-directions, respectively.
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 8 / 18

9. A new inversion method using mobile measurements (Kumar et al., 2020)
Inversion is based on two arguments:
1 Comparison between the observed plume transects and the Gaussian model
fitting the areas (i.e. cross-plume integrated) under measured and modeled peaks
2 Adjustment of the effective wind direction driving the Gaussian model
Select the optimal effective wind direction (θmi) as the direction from a possible
source to "middle" of the measured peak
Inversion is based on the minimization of a cost function (J):
Cost function : J = Jp + Jw
where Jp = P
i=1 |Aoi−Ami
Aoi
|2
= sum of normalized error between areas under each peak of modeled (Ami)
and observed plumes (Aoi)
Jw = P
i=1 | θo−θmi
σθ
|2 = sum of wind departure from the average measured wind
for each peak
θo = mean wind direction during release period
θmi = optimal effective wind direction from a possible source location to
the ith measured peak
σθ = standard deviation of the measured wind direction over the release period
Kumar, P., Broquet, G., Yver-Kwok, C., Laurent, O., Gichuki, S., Caldow, C., Cropley, F., Lauvaux, T., Ramonet, M., Berthe, G., Martin, F.,
Duclaux, O., Juery, C., Bouchet, C., and Ciais, P.: Mobile atmospheric measurements and local-scale inverse estimation of the location and
rates of brief CH4 and CO2 releases from point sources, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-226, 2020.
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 9 / 18

10. Inversions using the fixed-point measurements
Least-squares inversion:
Minimize the RSS of the misfits between the concentration from model and data at
the measurement locations binned over relatively short period of time windows of
equal lengths or within sectors of wind directions
Find the optimal estimate by looping over all the locations in a 3D dicretized space
Computation steps
Computational domain (ATEX zone): 40m×50m×8m (dx = dy = 1 m, dz = 0.5 m)
A receptor-oriented approach (using Gaussian model in "retro-transport" mode):
Compute sensitivity of the concentrations at each site to the emissions in each grid cell
At each grid location: Compute the theoretical optimal rate minimizing the RSS
using the adjoint of the Gaussian model
Estimate the release location and rate corresponding to the global minimum of a cost
function
Observation vector and meteorological and turbulence parameters
Wind variability is utilized to define the observation vector from limited number of sensors
Measurements at each tripod location are binned over (1) time windows of equal length (2)
wind direction sectors
Binning over the time windows: Nt = ts/∆t, where ts = release duration, ∆t = 15 min (7
min for low wind releases)
Binning over the wind sectors: bins of wind directions covering at least 4 1-min averages
are selected for the inversion
Compute the average concentration (obs. vector) and met & turb parameters in each
window/wind-sector
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 10 / 18

11. TADI-2018: Inversion results using mobile measurements (release-2)
Cost function and its components
Qs = Actual emission rate, Qe = Estimated rate, El = location error
0 5 10 15 20 25 30 35 40
x (m)
60
50
40
30
20
10
0
y(m)
El =27.67 m
Qe =0.30 g/s
Qs = 0.5 g/s release-2 Duration = 6.54 min
0.15
0.3
0.45
0.6
(a) Jp
0 5 10 15 20 25 30 35 40
x (m)
60
50
40
30
20
10
0
y(m)
El =27.67 m
Qe =0.30 g/s
Qs = 0.5 g/s release-2 Duration = 6.54 min
0
1.5
3
4.5
6
7.5
(b) Jw
0 5 10 15 20 25 30 35 40
x (m)
60
50
40
30
20
10
0
y(m)
El =27.67 m
Qe =0.30 g/s
Qs = 0.5 g/s release-2 Duration = 6.54 min
1.5
3
4.5
6
7.5
(c) J = Jp + Jw
Observed and modeled concentrations
14:11:10
14:11:20
14:11:30
14:11:40
14:11:50
14:12:00
14:12:10
Time (UTC)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Concentration(ppm)
Qs = 0.5 g/s release-2 (peak-1) Duration = 6.54 min
Observed
modeled
14:13:10
14:13:20
14:13:30
14:13:40
14:13:50
14:14:00
Time (UTC)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Concentration(ppm)
Qs = 0.5 g/s release-2 (peak-2) Duration = 6.54 min
Observed
modeled
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 11 / 18

12. TADI-2018: Inversion results using mobile measurements (for all releases)
CH4 releases
Release rate : <10% to ∼82% (overall relative error: 30.8%) (∼22% if ignoring 1 release-5)
Location error : 8.1 m to 62.9 m (average value: 29.8 m)
1 2 3 4 5 6 7
Release no.
100
101
Emissionrates(gCH4/s)
Actual
Estimated
CO2 releases
Release rate : <2% to ∼29% (overall relative error: ∼17%)
Location error : 21.5 m to 56.4 m (average value: ∼39 m)
8 9 10 11 12 13 14 15 16
Release no.
101
102
Emissionrates(gCO2/s)
Actual
Estimated
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 12 / 18

13. TADI-2019: Inversion results using mobile measurements (for all releases)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Release no.
100
101
Emissionrate(gCH4/s)
Actual
Estimated
overall relative error (Qe) =∼ 21%
overall location error (El) =∼ 30 m
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 13 / 18

14. TADI-2019: Inversion using fixed-point measurements
Cost function (normalized) (release-1)
Qs = actual release rate µtw
= obs. vector from binning data over the time-windows
Qe = estimated release rate µws
= obs. vector from binning data over the wind-sectors
El = location error
0 5 10 15 20 25 30 35 40
x (m)
50
40
30
20
10
0
y(m)
0.0
0.2
0.4
0.6
0.8
1.0
Qs = 10 g/s Qe = 6.52 g/s
El = 3.65 m
inversion using data binned over time windows
0 5 10 15 20 25 30 35 40
x (m)
50
40
30
20
10
0
y(m)
0.0
0.2
0.4
0.6
0.8
1.0
Qs = 10 g/s Qe = 5.90 g/s
El = 3.80 m
inversion using data binned over wind-sectors
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 14 / 18

15. TADI-2019: Inversion results using fixed-point measurements
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Release no.
100
101
Emissionrate(gCH4/s)
Actual
Estimated (fixed-points with µtw)
Estimated (fixed-points with µws)
overall relative error (Qe) =∼ 32%
overall location error (El) =∼ 10 m
overall relative error (Qe) =∼ 23%
overall location error (El) =∼ 8 m
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 15 / 18

16. TADI-2019: Summary all inversion results
10 1 100
101
Actual emission rate (qs) (g CH4 s 1)
0.0
0.5
1.0
1.5
2.0
2.5
Ratiooftheestimatedtotheactualemissionrate(qe/qs)
fixed-points (with µtw
) fixed-points (with µws
) mobile
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 16 / 18

17. Conclusions
New measurements (mobile and fixed-points) for controlled releases of CH4
and CO2 with a wide range of rates during the TADI-2018/2019 campaigns
A simple inversion modeling framework for localization and quantification of
CH4 and CO2 emissions from point sources based on mobile measurements
A least-squares inversion adapted to a limited number of fixed-point
measurements
Inversions using mobile measurements: a 20-30% average error on the estimate
of release rates, and ∼30-40m errors in the estimates of the release locations
Inversions using fixed-point measurements: An average relative error of ∼23%
in the emission rates, and an average location error of ∼8 m
Localization of the unknown releases is better obtained with the fixed-point
measurements than the mobile measurements
Mobile set-up is capable to estimate wide-range of emissions of very brief or
longer CH4 and CO2 release
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 17 / 18

18. Thank You!
Pramod Kumar, Grégoire Broquet, Christopher Caldow, Olivier Laurent, Camille Yver-Kwok, Ford Cropley, SarLocal-scale atmospheric inversion for the estimation of the locatioSeptember 16, 2020 18 / 18