variability in sub-surface gas transport in the light of
TRANSCRIPT
Variability in sub-surface gas transport in the light of field experiments and
numerical modelling Sophie Guillon1, Eric Pili2, Pierre Adler3, Florent Barbecot1, Roland Purtschert4, Lauren Raghoo4,
Yunwei Sun5, Charles Carrigan5
Disclaimer: The views expressed here do not necessarily reflect the opinion of the United States Government, the United States Department of Energy, or Lawrence Livermore National Laboratory.
1GEOTOP/UQAM, Montréal, Canada, 2 CEA, DAM, DIF, F-91297 Arpajon, France, 3 METIS, Université Pierre et Marie Curie, France, 4Climate and Environmental Physics, University of Bern, Switzerland,
5Lawrence Livermore National Laboratory, United StatesLLNL-PRES-672369
Why does sub-surface gas transport matter for CTBT?
• How to use 222Rn as a proxy to detect gas breakthrough?
• What is the range of variability of 37Ar or radioxenon natural background due to production and transport?
• How, where and when sampling soil gas to detect anomaly in 37Ar or relevant Xe isotopes?
• What are the mechanisms and factors controlling gas migration?
2
Numerical modelling of Barometric pumping in Fractured rocks
fractures porous medium
Patm
C=1
C=0
Tracer concentration after 50 days
ü Preferential migration to the surface into connected fractures
ü Discrete fracture network model
10-‐5 5x10-‐6 10-‐6 He Xe
ü Faster migration for gases with low diffusion coefficient
Diffusion coefficient
(m2/s)
20 mbar
100
10-1
10-2
10-3
10-4
10-5
10-6
C
X(m) X(m) X(m)
Z(m
)
Z(m
)
Z(m
) Mourzenko et al. (2014) 3
Variable Efficiency of Barometric pumping
ü Fast migration when fast and large drops of surface pressure
ü Strong control by boundary conditions (surface / water table, P / water)
Real barometric pressure cases:
50 days
20 mbar
20 mbar
X(m) X(m) X(m)
Concentration after 50 days
Z(m
)
Z(m
)
Z(m
)
time
Patm
t
Patm
t
Patm
20 mbar
4
222Rn dynamics in mountainous environment
ü Large seasonal (winter ~ 3-4 x summer) vs smaller daily fluctuations
4-10
m
Snow Snow
ü Spatial heterogeneity in fractured rocks 5
222Rn migration below snow cover & frozen soil
Fros
t dep
th, S
now
hei
ght (
cm)
Montreal
ü Partial capping effect of snow and frozen soil: 222Rn accumulates in winter
20 cm
70 cm
120 cm
Snow
Frozen soil
ü Control by diffusion, atmospheric ventilation and water fluxes
Snow capping & 222Rn accumulation Ventilation after snow removal
6
222Rn dynamics in the sub-surface controlled by water fluxes
7 m
ü Transient increases in 222Rn at depth following water infiltration
ü Reduced ventilation in saturated soil and advection of gas in borehole 7
Infiltration Rain
Wat
er in
filtr
atio
n (m
m)
Numerical modelling of 37Ar dynamics in unsaturated soil
ü 37Ar dynamics in controlled by water fluxes and soil water content ü Influence of half-life and solubility of gases
8
ü Numerical models with NUFT code
37A
r (B
q/m
3 )
2.4 m depth
Kr injection & migration at the Geosphere-Atmosphere interface
0 m
-‐5 m
Atmosphere
-‐10 m
Tarp
Ver4cal boreholes
Ver4cal boreholes
Tarp Accumula4on chambers
Sub-‐horizontal borehole
9
Fast migration in porous soil & transient emissions at the surface
Migra4on Time: 2 -‐30 h Dilu4on : 103-‐106
Rock Soil
Source term 2
31
2 3
4
Rock
Accumulation chamber
4
Injection borehole 1
ü Strong spatial heterogeneity
10
Soil
Conclusions & Recommendations
ü Barometric pumping cannot always drive gas to the surface.
ü Transient water infiltration events induce large variations of gas concentrations.
ü Important for OSI: - estimate local soil and atmosphere background at
time of sampling - ensure robustness & repeatability of measurement
ü More field data and modelling are required to better understand and predict gas migration.
ü Gas migration is controlled by rock properties & boundary conditions.
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