Radionuclide Gas Transport through Nuclear Explosion-Generated Fracture Networks

Abstract

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gas breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. Seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable.

Figure 1. Schematic of the integrated model.

The model sequentially couples a hydrodynamic rock damage code to a gas transport simulator via a translation between damage results and hydrogeologic parameters and stochastic sampling for uncertain hydrogeologic parameters.

Figure 2. Damage results at 125, 250, and 390 m depth of burial.

The chimney, which overlays the hydrocode-produced damage for transport calculations, is outlined in white. (a) Granite and (b) tuff.

Figure 3. Gas transport sensitivity results with uniformly varying hydrogeologic properties for granite and tuff.

Effect of varying (a) saturation, (b) matrix permeability, (c) porosity, and (d) maximum fracture aperture on ¹³³Xe arrival time and detection window of opportunity.

Figure 4. Comparison between granite and tuff at the minimum (0.8 mm) and maximum (3 mm) fracture aperture maximum (δ_f,max).

(a–d) Concentration plots for granite and tuff with varying δ_f,max, shortly after a barometric high pressure period (15 days) and during a barometric low (20 days). (e) Breakthrough curves at the centerline surface node for granite and tuff with δ_f,max of 0.8 and 3 mm and barometric pressure for the first 50 days.

Figure 5. Normalized detection probability plots from stochastic simulations.

(a) Barometric pressure for Denver, January; normalized detection probability, Ξ (t), for granite and tuff at all depths. (b) Barometric pressure for Anchorage and Honolulu, January; Ξ (t) for tuff (250 m) at all locations. (c) Barometric pressure for January and June detonations; Ξ (t) for tuff (250 m) at two locations and seasons.

Figure 6. Percent of stochastic simulations with detectable ¹³³Xe by day after the detonation.

(a) Different rock types (granite and tuff) and depths of burial, for a January detonation with Denver pressure data. (b) Different locations, for a January detonation, in tuff at 250 m depth of burial. (c) Different locations and seasons, January and June detonations in Anchorage and Honolulu, in tuff at 250 m depth of burial.

Figure 7. Simulation boundaries and initial gas distribution.