Modelling the attenuation of flowback chemicals for a soil-groundwater pathway from a hypothetical spill accident.

Flowback water from shale gas operations contains formation-derived compounds, including trace metals, radionuclides, and organics. While accidental releases from storage tanks with flowback water are low-probability events if multiple containment barriers are put in place, they cannot be entirely excluded. Here the natural attenuation potential of deep unsaturated zones and groundwater was explored using predictive modelling involving a hypothetical leak from a storage tank.

Quartz-Helium Method to Estimate Fluid Flow in Thick Aquitards, Gunnedah Basin, Australia.

The hydraulic integrity of aquitards is generally assumed and relies on a few core-scale permeability measurements, drill-stem tests, or textbook values. This approach is because hydraulic data across the full aquitard thickness is generally lacking. Proper assessment of aquitard integrity should be studied at the formation (spanning its entire thickness at a single point) or regional (formation properties at multiple locations throughout the basin) scale.

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful.

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock.

Spatial data captured with sensors of different resolution would provide a maximum degree of information if the data were to be merged into a single image representing all scales. We develop a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with rescaled correlation functions. The versatility of the method is demonstrated by merging three images of shale rock representing macro, micro and nanoscale spatial information on mineral, organic matter and porosity distribution.