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. Actual chemical concentrations from flowback water at two shale gas wells with contrasting salinity (12,300 and 105,000 ppm TDS) in the Beetaloo Sub-basin (Northern Territory, Australia) served as input to the one-dimensional HYDRUS model for simulating chemical transport through the unsaturated zone, with groundwater at 50 and 100 m depth, respectively. Subsequent chemical transport in groundwater involved the use of a three-dimensional analytical transport model. For a total of 63 chemicals the long-term attenuation from dilution and dispersion in unsaturated sediments and groundwater was calculated. Predicted environmental concentrations for aquatic receptors were compared with no-effect levels of individual chemicals to derive risk quotients (RQ) and identify chemicals of no concern to ecosystem health (i.e. RQ <1). Except for salinity and radium-228 in one of the two wells, RQ < 1 for all other chemicals. The initial approach considered testing of toxicity to individual chemicals only. When direct toxicity assessments (DTAs) were used to account for effects of chemical mixtures, the required DTA-derived safe dilution factor for 95% species protection was 1.8 to 2.5 times higher than the dilution factor accounting for dispersion and dilution only. Accounting for biodegradation, sorption and radioactive decay decreased chemical concentrations in unsaturated sediments to safe levels using the DTA for all chemicals. The study highlighted the importance of incorporating DTA in chemical risk assessments involving complex chemical mixtures. Improved understanding of fate and transport of flowback chemicals will help effectively manage water-quality risks associated with shale gas extraction.

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. One formation-scale approach uses environmental tracers and advection-dispersion modeling to constrain fluid flow rates. This study demonstrates the use of helium concentrations in quartz as a method of constraining the rate of fluid flow in a 520-m thick aquitard in the Gunnedah Basin, NSW, Australia. Quartz was separated from existing core samples in the Watermark and Porcupine Formations at depths from 750 to 1200 m. The helium was released from these samples by heating and select samples were impregnated with helium to determine the rate of helium diffusion through the quartz. One-dimensional advection-dispersion modeling of the helium profile accounting for diffusive helium exchange between quartz and pore water revealed, that (1) vertical fluid velocity has been on the order of 0.02 mm/year or less for tens to thousands of years, (2) helium is in equilibrium between quartz and pore water, and (3) the helium profile is transient indicating that helium concentrations in the underlying Maules Creek Formation has varied over geological time. Further modeling identified aquitard conditions (thickness and temperature) for which equilibrium exists, a precondition for deriving formation-scale permeability.

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. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

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. Merging multiscale images of shale rock is pivotal to quantify more reliably petrophysical properties needed for production optimization and environmental impacts minimization. Images obtained by X-ray microtomography and scanning electron microscopy were fused into a single image with predefined resolution. The methodology is sufficiently generic for implementation of other stochastic reconstruction techniques, any number of scales, any number of material phases, and any number of images for a given scale. The methodology can be further used to assess effective properties of fused porous media images or to compress voluminous spatial datasets for efficient data storage. Practical applications are not limited to petroleum engineering or more broadly geosciences, but will also find their way in material sciences, climatology, and remote sensing.

Universal spatial correlation functions for describing and reconstructing soil microstructure.

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity.